Overview

Dataset statistics

Number of variables69
Number of observations4964
Missing cells388
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory552.0 B

Variable types

Numeric19
Categorical39
Boolean11

Alerts

EEA_PolicyYear has constant value "2006"Constant
Policy_Zip_Code_Garaging_Location has a high cardinality: 699 distinct valuesHigh cardinality
Vehicle_Make_Description has a high cardinality: 1166 distinct valuesHigh cardinality
PolicyNo is highly overall correlated with EEA_Policy_Tenure and 2 other fieldsHigh correlation
Vehicle_Territory is highly overall correlated with EEA_Policy_Zip_Code_3High correlation
Vehicle_Make_Year is highly overall correlated with Vehicle_Symbol and 7 other fieldsHigh correlation
Vehicle_Symbol is highly overall correlated with Vehicle_Make_Year and 1 other fieldsHigh correlation
Vehicle_Miles_To_Work is highly overall correlated with Vehicle_UsageHigh correlation
Vehicle_Age_In_Years is highly overall correlated with Vehicle_Make_Year and 4 other fieldsHigh correlation
Vehicle_Physical_Damage_Limit is highly overall correlated with Vehicle_Bodily_Injury_Limit and 1 other fieldsHigh correlation
Vehicle_Comprehensive_Coverage_Limit is highly overall correlated with Vehicle_Bodily_Injury_Limit and 2 other fieldsHigh correlation
Vehicle_Collision_Coverage_Deductible is highly overall correlated with Vehicle_Make_Year and 5 other fieldsHigh correlation
Driver_Minimum_Age is highly overall correlated with Driver_Maximum_Age and 8 other fieldsHigh correlation
Driver_Maximum_Age is highly overall correlated with Driver_Minimum_Age and 7 other fieldsHigh correlation
EEA_Policy_Tenure is highly overall correlated with PolicyNo and 1 other fieldsHigh correlation
Annual_Premium is highly overall correlated with Vehicle_Make_Year and 7 other fieldsHigh correlation
Loss_Amount is highly overall correlated with Frequency and 2 other fieldsHigh correlation
Frequency is highly overall correlated with Loss_Amount and 2 other fieldsHigh correlation
Severity is highly overall correlated with Loss_Amount and 2 other fieldsHigh correlation
Loss_Ratio is highly overall correlated with Loss_Amount and 2 other fieldsHigh correlation
Policy_Installment_Term is highly overall correlated with Annual_Premium and 1 other fieldsHigh correlation
Policy_Billing_Code is highly overall correlated with Policy_Installment_TermHigh correlation
Vehicle_Usage is highly overall correlated with Vehicle_Miles_To_WorkHigh correlation
Vehicle_Passive_Restraint is highly overall correlated with Vehicle_Make_YearHigh correlation
Vehicle_Bodily_Injury_Limit is highly overall correlated with Vehicle_Physical_Damage_Limit and 3 other fieldsHigh correlation
Vehicle_Comprehensive_Coverage_Indicator is highly overall correlated with Vehicle_Comprehensive_Coverage_Limit and 2 other fieldsHigh correlation
Vehicle_Collision_Coverage_Indicator is highly overall correlated with Vehicle_Make_Year and 5 other fieldsHigh correlation
Driver_Total is highly overall correlated with Driver_Total_Married and 1 other fieldsHigh correlation
Driver_Total_Male is highly overall correlated with Driver_Total_FemaleHigh correlation
Driver_Total_Female is highly overall correlated with Driver_Total_MaleHigh correlation
Driver_Total_Single is highly overall correlated with Vehicle_Youthful_Driver_IndicatorHigh correlation
Driver_Total_Married is highly overall correlated with Driver_Total and 1 other fieldsHigh correlation
Driver_Total_Related_To_Insured_Child is highly overall correlated with Driver_Total_Teenager_Age_15_19 and 2 other fieldsHigh correlation
Driver_Total_Licensed_In_State is highly overall correlated with Driver_Total and 1 other fieldsHigh correlation
Driver_Total_Teenager_Age_15_19 is highly overall correlated with Driver_Total_Related_To_Insured_Child and 2 other fieldsHigh correlation
Driver_Total_College_Ages_20_23 is highly overall correlated with Vehicle_Youthful_Driver_IndicatorHigh correlation
Driver_Total_Young_Adult_Ages_24_29 is highly overall correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total_Low_Middle_Adult_Ages_30_39 is highly overall correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total_Middle_Adult_Ages_40_49 is highly overall correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total_Adult_Ages_50_64 is highly overall correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total_Senior_Ages_65_69 is highly overall correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Driver_Total_Upper_Senior_Ages_70_plus is highly overall correlated with Driver_Minimum_Age and 1 other fieldsHigh correlation
Vehicle_Youthful_Driver_Indicator is highly overall correlated with Driver_Minimum_Age and 6 other fieldsHigh correlation
Vehicle_Youthful_Driver_Training_Code is highly overall correlated with Driver_Minimum_Age and 3 other fieldsHigh correlation
Vehicle_Safe_Driver_Discount_Indicator is highly overall correlated with PolicyNo and 1 other fieldsHigh correlation
EEA_Liability_Coverage_Only_Indicator is highly overall correlated with Vehicle_Make_Year and 5 other fieldsHigh correlation
EEA_Policy_Zip_Code_3 is highly overall correlated with Vehicle_TerritoryHigh correlation
EEA_Packaged_Policy_Indicator is highly overall correlated with EEA_Liability_Coverage_Only_IndicatorHigh correlation
EEA_Full_Coverage_Indicator is highly overall correlated with Vehicle_Make_Year and 5 other fieldsHigh correlation
EEA_Prior_Bodily_Injury_Limit is highly overall correlated with Vehicle_Physical_Damage_Limit and 3 other fieldsHigh correlation
SYS_New_Business is highly overall correlated with PolicyNoHigh correlation
Policy_Company is highly imbalanced (68.3%)Imbalance
Policy_Installment_Term is highly imbalanced (77.2%)Imbalance
Policy_Billing_Code is highly imbalanced (85.7%)Imbalance
Vehicle_Performance is highly imbalanced (84.9%)Imbalance
Vehicle_Number_Of_Drivers_Assigned is highly imbalanced (56.9%)Imbalance
Vehicle_Days_Per_Week_Driven is highly imbalanced (99.7%)Imbalance
Vehicle_Annual_Miles is highly imbalanced (99.5%)Imbalance
Vehicle_Comprehensive_Coverage_Indicator is highly imbalanced (76.8%)Imbalance
Driver_Total is highly imbalanced (66.5%)Imbalance
Driver_Total_Related_To_Insured_Child is highly imbalanced (75.9%)Imbalance
Driver_Total_Licensed_In_State is highly imbalanced (72.3%)Imbalance
Driver_Total_Teenager_Age_15_19 is highly imbalanced (84.1%)Imbalance
Driver_Total_College_Ages_20_23 is highly imbalanced (84.1%)Imbalance
Driver_Total_Young_Adult_Ages_24_29 is highly imbalanced (71.2%)Imbalance
Driver_Total_Senior_Ages_65_69 is highly imbalanced (78.4%)Imbalance
Driver_Total_Upper_Senior_Ages_70_plus is highly imbalanced (72.4%)Imbalance
Vehicle_Youthful_Driver_Indicator is highly imbalanced (53.8%)Imbalance
Vehicle_Youthful_Driver_Training_Code is highly imbalanced (70.4%)Imbalance
Vehicle_Youthful_Good_Student_Code is highly imbalanced (90.3%)Imbalance
Vehicle_Driver_Points is highly imbalanced (72.1%)Imbalance
SYS_New_Business is highly imbalanced (55.1%)Imbalance
Claim_Count is highly imbalanced (80.9%)Imbalance
Vehicle_Bodily_Injury_Limit has 194 (3.9%) missing valuesMissing
EEA_Prior_Bodily_Injury_Limit has 194 (3.9%) missing valuesMissing
Loss_Amount is highly skewed (γ1 = 51.00506603)Skewed
Frequency is highly skewed (γ1 = 46.5164757)Skewed
Severity is highly skewed (γ1 = 51.69328358)Skewed
Loss_Ratio is highly skewed (γ1 = 40.7590306)Skewed
PolicyNo has unique valuesUnique
EEA_Policy_Tenure has 459 (9.2%) zerosZeros
Loss_Amount has 4715 (95.0%) zerosZeros
Frequency has 4715 (95.0%) zerosZeros
Severity has 4715 (95.0%) zerosZeros
Loss_Ratio has 4715 (95.0%) zerosZeros

Reproduction

Analysis started2023-01-27 16:17:27.799342
Analysis finished2023-01-27 16:18:35.671459
Duration1 minute and 7.87 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

PolicyNo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct4964
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1589213 × 108
Minimum1.6456203 × 108
Maximum3.812589 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:35.799033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.6456203 × 108
5-th percentile1.9392028 × 108
Q12.8633649 × 108
median3.3418281 × 108
Q33.611901 × 108
95-th percentile3.7603538 × 108
Maximum3.812589 × 108
Range2.1669687 × 108
Interquartile range (IQR)74853612

Descriptive statistics

Standard deviation56810825
Coefficient of variation (CV)0.17984248
Kurtosis-0.014050115
Mean3.1589213 × 108
Median Absolute Deviation (MAD)31945944
Skewness-1.0081116
Sum1.5680886 × 1012
Variance3.2274698 × 1015
MonotonicityStrictly increasing
2023-01-27T11:18:35.936251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164562033 1
 
< 0.1%
353676002 1
 
< 0.1%
353839804 1
 
< 0.1%
353805003 1
 
< 0.1%
353796002 1
 
< 0.1%
353732604 1
 
< 0.1%
353686203 1
 
< 0.1%
353679104 1
 
< 0.1%
353678003 1
 
< 0.1%
353675603 1
 
< 0.1%
Other values (4954) 4954
99.8%
ValueCountFrequency (%)
164562033 1
< 0.1%
165119133 1
< 0.1%
165166239 1
< 0.1%
165198832 1
< 0.1%
165319534 1
< 0.1%
165355034 1
< 0.1%
165386232 1
< 0.1%
165708632 1
< 0.1%
165951132 1
< 0.1%
165971032 1
< 0.1%
ValueCountFrequency (%)
381258900 1
< 0.1%
381184700 1
< 0.1%
381148600 1
< 0.1%
381140200 1
< 0.1%
381137600 1
< 0.1%
381080800 1
< 0.1%
381052900 1
< 0.1%
381040600 1
< 0.1%
381039600 1
< 0.1%
381020700 1
< 0.1%

Policy_Company
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Standard
4679 
Preferred
 
285

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters44676
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowStandard

Common Values

ValueCountFrequency (%)
Standard 4679
94.3%
Preferred 285
 
5.7%

Length

2023-01-27T11:18:36.049807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:36.146745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 4679
94.3%
preferred 285
 
5.7%

Most occurring characters

ValueCountFrequency (%)
d 9643
21.6%
a 9358
20.9%
r 5534
12.4%
S 4679
10.5%
t 4679
10.5%
n 4679
10.5%
4679
10.5%
e 855
 
1.9%
P 285
 
0.6%
f 285
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35033
78.4%
Uppercase Letter 4964
 
11.1%
Space Separator 4679
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 9643
27.5%
a 9358
26.7%
r 5534
15.8%
t 4679
13.4%
n 4679
13.4%
e 855
 
2.4%
f 285
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S 4679
94.3%
P 285
 
5.7%
Space Separator
ValueCountFrequency (%)
4679
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39997
89.5%
Common 4679
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 9643
24.1%
a 9358
23.4%
r 5534
13.8%
S 4679
11.7%
t 4679
11.7%
n 4679
11.7%
e 855
 
2.1%
P 285
 
0.7%
f 285
 
0.7%
Common
ValueCountFrequency (%)
4679
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44676
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 9643
21.6%
a 9358
20.9%
r 5534
12.4%
S 4679
10.5%
t 4679
10.5%
n 4679
10.5%
4679
10.5%
e 855
 
1.9%
P 285
 
0.6%
f 285
 
0.6%

Policy_Installment_Term
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
6
4781 
12
 
183

Length

Max length2
Median length1
Mean length1.0368654
Min length1

Characters and Unicode

Total characters5147
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 4781
96.3%
12 183
 
3.7%

Length

2023-01-27T11:18:36.257699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:36.355244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
6 4781
96.3%
12 183
 
3.7%

Most occurring characters

ValueCountFrequency (%)
6 4781
92.9%
1 183
 
3.6%
2 183
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5147
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 4781
92.9%
1 183
 
3.6%
2 183
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 4781
92.9%
1 183
 
3.6%
2 183
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 4781
92.9%
1 183
 
3.6%
2 183
 
3.6%

Policy_Billing_Code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Direct Billed to Insured
4863 
Premium Finance
 
101

Length

Max length24
Median length24
Mean length23.816882
Min length15

Characters and Unicode

Total characters118227
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Billed to Insured
2nd rowDirect Billed to Insured
3rd rowDirect Billed to Insured
4th rowDirect Billed to Insured
5th rowDirect Billed to Insured

Common Values

ValueCountFrequency (%)
Direct Billed to Insured 4863
98.0%
Premium Finance 101
 
2.0%

Length

2023-01-27T11:18:36.434778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:36.527531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
direct 4863
24.7%
billed 4863
24.7%
to 4863
24.7%
insured 4863
24.7%
premium 101
 
0.5%
finance 101
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 14791
12.5%
14690
12.4%
i 9928
 
8.4%
r 9827
 
8.3%
d 9726
 
8.2%
t 9726
 
8.2%
l 9726
 
8.2%
n 5065
 
4.3%
c 4964
 
4.2%
u 4964
 
4.2%
Other values (9) 24820
21.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88746
75.1%
Uppercase Letter 14791
 
12.5%
Space Separator 14690
 
12.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14791
16.7%
i 9928
11.2%
r 9827
11.1%
d 9726
11.0%
t 9726
11.0%
l 9726
11.0%
n 5065
 
5.7%
c 4964
 
5.6%
u 4964
 
5.6%
s 4863
 
5.5%
Other values (3) 5166
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
D 4863
32.9%
I 4863
32.9%
B 4863
32.9%
P 101
 
0.7%
F 101
 
0.7%
Space Separator
ValueCountFrequency (%)
14690
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 103537
87.6%
Common 14690
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14791
14.3%
i 9928
9.6%
r 9827
9.5%
d 9726
9.4%
t 9726
9.4%
l 9726
9.4%
n 5065
 
4.9%
c 4964
 
4.8%
u 4964
 
4.8%
s 4863
 
4.7%
Other values (8) 19957
19.3%
Common
ValueCountFrequency (%)
14690
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 118227
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 14791
12.5%
14690
12.4%
i 9928
 
8.4%
r 9827
 
8.3%
d 9726
 
8.2%
t 9726
 
8.2%
l 9726
 
8.2%
n 5065
 
4.3%
c 4964
 
4.2%
u 4964
 
4.2%
Other values (9) 24820
21.0%

Policy_Method_Of_Payment
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Installment
2572 
Pre-paid
2392 

Length

Max length11
Median length11
Mean length9.5543916
Min length8

Characters and Unicode

Total characters47428
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPre-paid
2nd rowPre-paid
3rd rowPre-paid
4th rowInstallment
5th rowPre-paid

Common Values

ValueCountFrequency (%)
Installment 2572
51.8%
Pre-paid 2392
48.2%

Length

2023-01-27T11:18:36.609524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:36.709055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
installment 2572
51.8%
pre-paid 2392
48.2%

Most occurring characters

ValueCountFrequency (%)
n 5144
10.8%
t 5144
10.8%
l 5144
10.8%
a 4964
10.5%
e 4964
10.5%
I 2572
 
5.4%
s 2572
 
5.4%
m 2572
 
5.4%
P 2392
 
5.0%
r 2392
 
5.0%
Other values (4) 9568
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40072
84.5%
Uppercase Letter 4964
 
10.5%
Dash Punctuation 2392
 
5.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5144
12.8%
t 5144
12.8%
l 5144
12.8%
a 4964
12.4%
e 4964
12.4%
s 2572
6.4%
m 2572
6.4%
r 2392
6.0%
p 2392
6.0%
i 2392
6.0%
Uppercase Letter
ValueCountFrequency (%)
I 2572
51.8%
P 2392
48.2%
Dash Punctuation
ValueCountFrequency (%)
- 2392
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 45036
95.0%
Common 2392
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5144
11.4%
t 5144
11.4%
l 5144
11.4%
a 4964
11.0%
e 4964
11.0%
I 2572
 
5.7%
s 2572
 
5.7%
m 2572
 
5.7%
P 2392
 
5.3%
r 2392
 
5.3%
Other values (3) 7176
15.9%
Common
ValueCountFrequency (%)
- 2392
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5144
10.8%
t 5144
10.8%
l 5144
10.8%
a 4964
10.5%
e 4964
10.5%
I 2572
 
5.4%
s 2572
 
5.4%
m 2572
 
5.4%
P 2392
 
5.0%
r 2392
 
5.0%
Other values (4) 9568
20.2%

Policy_Reinstatement_Fee_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4385 
True
579 
ValueCountFrequency (%)
False 4385
88.3%
True 579
 
11.7%
2023-01-27T11:18:36.795369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Policy_Zip_Code_Garaging_Location
Categorical

Distinct699
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
43025
 
52
43050
 
48
42878
 
43
42922
 
43
43046
 
42
Other values (694)
4736 

Length

Max length7
Median length5
Mean length5.0108783
Min length5

Characters and Unicode

Total characters24874
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)2.6%

Sample

1st row42602
2nd row42857
3rd row42980
4th row43050
5th row42496

Common Values

ValueCountFrequency (%)
43025 52
 
1.0%
43050 48
 
1.0%
42878 43
 
0.9%
42922 43
 
0.9%
43046 42
 
0.8%
42873 40
 
0.8%
43066 38
 
0.8%
42988 37
 
0.7%
42462 34
 
0.7%
43169 33
 
0.7%
Other values (689) 4554
91.7%

Length

2023-01-27T11:18:36.890882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
43025 52
 
1.0%
43050 48
 
1.0%
42878 43
 
0.9%
42922 43
 
0.9%
43046 42
 
0.8%
42873 40
 
0.8%
43066 38
 
0.8%
42988 37
 
0.7%
42462 34
 
0.7%
43169 33
 
0.7%
Other values (689) 4554
91.7%

Most occurring characters

ValueCountFrequency (%)
4 6605
26.6%
2 3537
14.2%
3 3067
12.3%
8 2189
 
8.8%
5 1768
 
7.1%
0 1645
 
6.6%
6 1630
 
6.6%
9 1574
 
6.3%
7 1420
 
5.7%
1 1250
 
5.0%
Other values (5) 189
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24685
99.2%
Lowercase Letter 162
 
0.7%
Uppercase Letter 27
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6605
26.8%
2 3537
14.3%
3 3067
12.4%
8 2189
 
8.9%
5 1768
 
7.2%
0 1645
 
6.7%
6 1630
 
6.6%
9 1574
 
6.4%
7 1420
 
5.8%
1 1250
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
n 81
50.0%
k 27
 
16.7%
o 27
 
16.7%
w 27
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24685
99.2%
Latin 189
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6605
26.8%
2 3537
14.3%
3 3067
12.4%
8 2189
 
8.9%
5 1768
 
7.2%
0 1645
 
6.7%
6 1630
 
6.6%
9 1574
 
6.4%
7 1420
 
5.8%
1 1250
 
5.1%
Latin
ValueCountFrequency (%)
n 81
42.9%
U 27
 
14.3%
k 27
 
14.3%
o 27
 
14.3%
w 27
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6605
26.6%
2 3537
14.2%
3 3067
12.3%
8 2189
 
8.8%
5 1768
 
7.1%
0 1645
 
6.6%
6 1630
 
6.6%
9 1574
 
6.3%
7 1420
 
5.7%
1 1250
 
5.0%
Other values (5) 189
 
0.8%

Vehicle_Territory
Real number (ℝ)

Distinct16
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.103747
Minimum13
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:36.987123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20
Q130
median31
Q335
95-th percentile35
Maximum37
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.1777832
Coefficient of variation (CV)0.13431768
Kurtosis6.1140065
Mean31.103747
Median Absolute Deviation (MAD)1
Skewness-2.1290508
Sum154399
Variance17.453873
MonotonicityNot monotonic
2023-01-27T11:18:37.080237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
31 2096
42.2%
35 1304
26.3%
30 775
 
15.6%
27 197
 
4.0%
32 120
 
2.4%
37 105
 
2.1%
17 62
 
1.2%
13 54
 
1.1%
26 54
 
1.1%
20 44
 
0.9%
Other values (6) 153
 
3.1%
ValueCountFrequency (%)
13 54
 
1.1%
15 21
 
0.4%
16 34
 
0.7%
17 62
 
1.2%
18 14
 
0.3%
19 28
 
0.6%
20 44
 
0.9%
22 15
 
0.3%
26 54
 
1.1%
27 197
4.0%
ValueCountFrequency (%)
37 105
 
2.1%
36 41
 
0.8%
35 1304
26.3%
32 120
 
2.4%
31 2096
42.2%
30 775
 
15.6%
27 197
 
4.0%
26 54
 
1.1%
22 15
 
0.3%
20 44
 
0.9%

Vehicle_Make_Year
Real number (ℝ)

Distinct50
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1996.7037
Minimum1939
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:37.190080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1939
5-th percentile1984
Q11993
median1998
Q32002
95-th percentile2005
Maximum2007
Range68
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.4026844
Coefficient of variation (CV)0.0037074527
Kurtosis4.0225976
Mean1996.7037
Median Absolute Deviation (MAD)4
Skewness-1.5593359
Sum9911637
Variance54.799737
MonotonicityNot monotonic
2023-01-27T11:18:37.313839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 356
 
7.2%
1999 314
 
6.3%
2002 307
 
6.2%
2001 305
 
6.1%
1997 298
 
6.0%
1998 297
 
6.0%
2004 296
 
6.0%
2005 281
 
5.7%
2003 267
 
5.4%
1995 253
 
5.1%
Other values (40) 1990
40.1%
ValueCountFrequency (%)
1939 1
 
< 0.1%
1955 2
 
< 0.1%
1956 1
 
< 0.1%
1957 2
 
< 0.1%
1958 1
 
< 0.1%
1963 3
 
0.1%
1964 5
0.1%
1965 3
 
0.1%
1966 12
0.2%
1967 3
 
0.1%
ValueCountFrequency (%)
2007 44
 
0.9%
2006 193
3.9%
2005 281
5.7%
2004 296
6.0%
2003 267
5.4%
2002 307
6.2%
2001 305
6.1%
2000 356
7.2%
1999 314
6.3%
1998 297
6.0%

Vehicle_Make_Description
Categorical

Distinct1166
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
FORD F150
 
165
CHEV PICKUP1500
 
130
FORD RANGER
 
120
CHEV S10 PICKUP
 
105
CHEV SILVER1500
 
91
Other values (1161)
4353 

Length

Max length18
Median length17
Mean length16.999799
Min length16

Characters and Unicode

Total characters84387
Distinct characters43
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique574 ?
Unique (%)11.6%

Sample

1st rowDODG CARAVAN SE
2nd rowBUIK REG LS-LSE
3rd rowFORD TRUCK
4th rowTYTA TUNDRA SR5
5th rowCHEV CAMARO RS

Common Values

ValueCountFrequency (%)
FORD F150 165
 
3.3%
CHEV PICKUP1500 130
 
2.6%
FORD RANGER 120
 
2.4%
CHEV S10 PICKUP 105
 
2.1%
CHEV SILVER1500 91
 
1.8%
TYTA CAMRY 78
 
1.6%
DODG RAM PU1500 75
 
1.5%
HOND ACCORD EX 75
 
1.5%
FORD EXPLORER 71
 
1.4%
HOND ACCORD LX 63
 
1.3%
Other values (1156) 3991
80.4%

Length

2023-01-27T11:18:37.451208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ford 1171
 
9.5%
chev 1044
 
8.4%
tyta 366
 
3.0%
dodg 317
 
2.6%
hond 266
 
2.2%
f150 235
 
1.9%
pickup 215
 
1.7%
nssn 210
 
1.7%
jeep 196
 
1.6%
gmc 190
 
1.5%
Other values (1042) 8153
65.9%

Most occurring characters

ValueCountFrequency (%)
26120
31.0%
R 5044
 
6.0%
E 4577
 
5.4%
C 3725
 
4.4%
A 3692
 
4.4%
O 3465
 
4.1%
D 3240
 
3.8%
S 3189
 
3.8%
T 2765
 
3.3%
L 2595
 
3.1%
Other values (33) 25975
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 53159
63.0%
Space Separator 26120
31.0%
Decimal Number 4456
 
5.3%
Dash Punctuation 597
 
0.7%
Other Punctuation 53
 
0.1%
Math Symbol 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 5044
 
9.5%
E 4577
 
8.6%
C 3725
 
7.0%
A 3692
 
6.9%
O 3465
 
6.5%
D 3240
 
6.1%
S 3189
 
6.0%
T 2765
 
5.2%
L 2595
 
4.9%
N 2373
 
4.5%
Other values (16) 18494
34.8%
Decimal Number
ValueCountFrequency (%)
0 1840
41.3%
1 997
22.4%
5 975
21.9%
2 264
 
5.9%
3 147
 
3.3%
4 100
 
2.2%
8 59
 
1.3%
6 28
 
0.6%
7 25
 
0.6%
9 21
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 33
62.3%
& 16
30.2%
\ 4
 
7.5%
Space Separator
ValueCountFrequency (%)
26120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 597
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 53159
63.0%
Common 31228
37.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 5044
 
9.5%
E 4577
 
8.6%
C 3725
 
7.0%
A 3692
 
6.9%
O 3465
 
6.5%
D 3240
 
6.1%
S 3189
 
6.0%
T 2765
 
5.2%
L 2595
 
4.9%
N 2373
 
4.5%
Other values (16) 18494
34.8%
Common
ValueCountFrequency (%)
26120
83.6%
0 1840
 
5.9%
1 997
 
3.2%
5 975
 
3.1%
- 597
 
1.9%
2 264
 
0.8%
3 147
 
0.5%
4 100
 
0.3%
8 59
 
0.2%
. 33
 
0.1%
Other values (7) 96
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 84387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26120
31.0%
R 5044
 
6.0%
E 4577
 
5.4%
C 3725
 
4.4%
A 3692
 
4.4%
O 3465
 
4.1%
D 3240
 
3.8%
S 3189
 
3.8%
T 2765
 
3.3%
L 2595
 
3.1%
Other values (33) 25975
30.8%

Vehicle_Performance
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Standard
4725 
Intermediate
 
134
High
 
72
Sports Premium
 
22
Sports
 
11

Length

Max length14
Median length8
Mean length8.0721193
Min length4

Characters and Unicode

Total characters40070
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStandard
2nd rowStandard
3rd rowStandard
4th rowStandard
5th rowSports

Common Values

ValueCountFrequency (%)
Standard 4725
95.2%
Intermediate 134
 
2.7%
High 72
 
1.5%
Sports Premium 22
 
0.4%
Sports 11
 
0.2%

Length

2023-01-27T11:18:37.563202image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:37.667760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
standard 4725
94.8%
intermediate 134
 
2.7%
high 72
 
1.4%
sports 33
 
0.7%
premium 22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a 9584
23.9%
d 9584
23.9%
t 5026
12.5%
r 4914
12.3%
n 4859
12.1%
S 4758
11.9%
e 424
 
1.1%
i 228
 
0.6%
m 178
 
0.4%
I 134
 
0.3%
Other values (9) 381
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 35062
87.5%
Uppercase Letter 4986
 
12.4%
Space Separator 22
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9584
27.3%
d 9584
27.3%
t 5026
14.3%
r 4914
14.0%
n 4859
13.9%
e 424
 
1.2%
i 228
 
0.7%
m 178
 
0.5%
g 72
 
0.2%
h 72
 
0.2%
Other values (4) 121
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 4758
95.4%
I 134
 
2.7%
H 72
 
1.4%
P 22
 
0.4%
Space Separator
ValueCountFrequency (%)
22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40048
99.9%
Common 22
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9584
23.9%
d 9584
23.9%
t 5026
12.5%
r 4914
12.3%
n 4859
12.1%
S 4758
11.9%
e 424
 
1.1%
i 228
 
0.6%
m 178
 
0.4%
I 134
 
0.3%
Other values (8) 359
 
0.9%
Common
ValueCountFrequency (%)
22
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40070
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9584
23.9%
d 9584
23.9%
t 5026
12.5%
r 4914
12.3%
n 4859
12.1%
S 4758
11.9%
e 424
 
1.1%
i 228
 
0.6%
m 178
 
0.4%
I 134
 
0.3%
Other values (9) 381
 
1.0%

Vehicle_New_Cost_Amount
Real number (ℝ)

Distinct32
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean263.00121
Minimum-1
Maximum45000
Zeros7
Zeros (%)0.1%
Negative4883
Negative (%)98.4%
Memory size38.9 KiB
2023-01-27T11:18:37.768337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum45000
Range45001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2418.7172
Coefficient of variation (CV)9.1966011
Kurtosis118.79912
Mean263.00121
Median Absolute Deviation (MAD)0
Skewness10.396263
Sum1305538
Variance5850192.9
MonotonicityNot monotonic
2023-01-27T11:18:37.870726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
-1 4883
98.4%
0 7
 
0.1%
15000 6
 
0.1%
25000 6
 
0.1%
30000 5
 
0.1%
20000 5
 
0.1%
16000 5
 
0.1%
10000 5
 
0.1%
24000 4
 
0.1%
8000 4
 
0.1%
Other values (22) 34
 
0.7%
ValueCountFrequency (%)
-1 4883
98.4%
0 7
 
0.1%
2 1
 
< 0.1%
19 1
 
< 0.1%
2000 1
 
< 0.1%
3000 2
 
< 0.1%
3500 1
 
< 0.1%
4000 1
 
< 0.1%
4400 1
 
< 0.1%
6000 1
 
< 0.1%
ValueCountFrequency (%)
45000 1
 
< 0.1%
40000 1
 
< 0.1%
32000 1
 
< 0.1%
30000 5
0.1%
28500 1
 
< 0.1%
28000 2
 
< 0.1%
26000 2
 
< 0.1%
25000 6
0.1%
24000 4
0.1%
23000 1
 
< 0.1%

Vehicle_Symbol
Real number (ℝ)

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.252015
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:37.968345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median11
Q314
95-th percentile18
Maximum26
Range25
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0468177
Coefficient of variation (CV)0.35965273
Kurtosis-0.021189689
Mean11.252015
Median Absolute Deviation (MAD)3
Skewness0.22902006
Sum55855
Variance16.376734
MonotonicityNot monotonic
2023-01-27T11:18:38.072953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11 570
11.5%
10 561
11.3%
8 535
10.8%
12 483
9.7%
14 447
9.0%
13 405
8.2%
7 346
7.0%
15 312
 
6.3%
6 251
 
5.1%
16 243
 
4.9%
Other values (15) 811
16.3%
ValueCountFrequency (%)
1 5
 
0.1%
2 29
 
0.6%
3 43
 
0.9%
4 122
 
2.5%
5 171
 
3.4%
6 251
5.1%
7 346
7.0%
8 535
10.8%
10 561
11.3%
11 570
11.5%
ValueCountFrequency (%)
26 5
 
0.1%
25 3
 
0.1%
24 5
 
0.1%
23 11
 
0.2%
22 34
 
0.7%
21 35
 
0.7%
20 48
 
1.0%
19 61
1.2%
18 102
2.1%
17 137
2.8%

Vehicle_Number_Of_Drivers_Assigned
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
3464 
99
1399 
2
 
94
3
 
6
4
 
1

Length

Max length2
Median length1
Mean length1.2818292
Min length1

Characters and Unicode

Total characters6363
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row99
4th row99
5th row1

Common Values

ValueCountFrequency (%)
1 3464
69.8%
99 1399
28.2%
2 94
 
1.9%
3 6
 
0.1%
4 1
 
< 0.1%

Length

2023-01-27T11:18:38.176175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:38.280634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3464
69.8%
99 1399
28.2%
2 94
 
1.9%
3 6
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 3464
54.4%
9 2798
44.0%
2 94
 
1.5%
3 6
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6363
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3464
54.4%
9 2798
44.0%
2 94
 
1.5%
3 6
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 6363
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3464
54.4%
9 2798
44.0%
2 94
 
1.5%
3 6
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6363
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3464
54.4%
9 2798
44.0%
2 94
 
1.5%
3 6
 
0.1%
4 1
 
< 0.1%

Vehicle_Usage
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Pleasure
2659 
Work
1824 
Farm
459 
Business
 
22

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters39712
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPleasure
2nd rowPleasure
3rd rowFarm
4th rowPleasure
5th rowPleasure

Common Values

ValueCountFrequency (%)
Pleasure 2659
53.6%
Work 1824
36.7%
Farm 459
 
9.2%
Business 22
 
0.4%

Length

2023-01-27T11:18:38.369577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:38.471153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
pleasure 2659
53.6%
work 1824
36.7%
farm 459
 
9.2%
business 22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
9132
23.0%
e 5340
13.4%
r 4942
12.4%
a 3118
 
7.9%
s 2725
 
6.9%
u 2681
 
6.8%
P 2659
 
6.7%
l 2659
 
6.7%
W 1824
 
4.6%
o 1824
 
4.6%
Other values (6) 2808
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25616
64.5%
Space Separator 9132
 
23.0%
Uppercase Letter 4964
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5340
20.8%
r 4942
19.3%
a 3118
12.2%
s 2725
10.6%
u 2681
10.5%
l 2659
10.4%
o 1824
 
7.1%
k 1824
 
7.1%
m 459
 
1.8%
i 22
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 2659
53.6%
W 1824
36.7%
F 459
 
9.2%
B 22
 
0.4%
Space Separator
ValueCountFrequency (%)
9132
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 30580
77.0%
Common 9132
 
23.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5340
17.5%
r 4942
16.2%
a 3118
10.2%
s 2725
8.9%
u 2681
8.8%
P 2659
8.7%
l 2659
8.7%
W 1824
 
6.0%
o 1824
 
6.0%
k 1824
 
6.0%
Other values (5) 984
 
3.2%
Common
ValueCountFrequency (%)
9132
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39712
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9132
23.0%
e 5340
13.4%
r 4942
12.4%
a 3118
 
7.9%
s 2725
 
6.9%
u 2681
 
6.8%
P 2659
 
6.7%
l 2659
 
6.7%
W 1824
 
4.6%
o 1824
 
4.6%
Other values (6) 2808
 
7.1%

Vehicle_Miles_To_Work
Real number (ℝ)

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2530218
Minimum-1
Maximum70
Zeros9
Zeros (%)0.2%
Negative3107
Negative (%)62.6%
Memory size38.9 KiB
2023-01-27T11:18:38.572657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q36
95-th percentile15
Maximum70
Range71
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.5168185
Coefficient of variation (CV)2.3107188
Kurtosis12.384615
Mean3.2530218
Median Absolute Deviation (MAD)0
Skewness2.8786655
Sum16148
Variance56.50256
MonotonicityNot monotonic
2023-01-27T11:18:38.709230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
-1 3107
62.6%
10 362
 
7.3%
5 224
 
4.5%
12 166
 
3.3%
8 131
 
2.6%
6 106
 
2.1%
3 105
 
2.1%
14 104
 
2.1%
4 88
 
1.8%
7 74
 
1.5%
Other values (34) 497
 
10.0%
ValueCountFrequency (%)
-1 3107
62.6%
0 9
 
0.2%
1 53
 
1.1%
2 74
 
1.5%
3 105
 
2.1%
4 88
 
1.8%
5 224
 
4.5%
6 106
 
2.1%
7 74
 
1.5%
8 131
 
2.6%
ValueCountFrequency (%)
70 2
 
< 0.1%
63 1
 
< 0.1%
60 1
 
< 0.1%
55 1
 
< 0.1%
53 1
 
< 0.1%
50 15
0.3%
48 1
 
< 0.1%
45 6
 
0.1%
42 1
 
< 0.1%
40 10
0.2%

Vehicle_Days_Per_Week_Driven
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
5
4963 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 4963
> 99.9%
1 1
 
< 0.1%

Length

2023-01-27T11:18:38.834296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:38.923346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
5 4963
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
5 4963
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4963
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4963
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4963
> 99.9%
1 1
 
< 0.1%

Vehicle_Annual_Miles
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Unknown
4962 
0
 
2

Length

Max length7
Median length7
Mean length6.9975826
Min length1

Characters and Unicode

Total characters34736
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 4962
> 99.9%
0 2
 
< 0.1%

Length

2023-01-27T11:18:39.019887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:39.119410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
unknown 4962
> 99.9%
0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 14886
42.9%
U 4962
 
14.3%
k 4962
 
14.3%
o 4962
 
14.3%
w 4962
 
14.3%
0 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29772
85.7%
Uppercase Letter 4962
 
14.3%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 14886
50.0%
k 4962
 
16.7%
o 4962
 
16.7%
w 4962
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U 4962
100.0%
Decimal Number
ValueCountFrequency (%)
0 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34734
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 14886
42.9%
U 4962
 
14.3%
k 4962
 
14.3%
o 4962
 
14.3%
w 4962
 
14.3%
Common
ValueCountFrequency (%)
0 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 14886
42.9%
U 4962
 
14.3%
k 4962
 
14.3%
o 4962
 
14.3%
w 4962
 
14.3%
0 2
 
< 0.1%

Vehicle_Anti_Theft_Device
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Not Applicable
2902 
Passive Disabling-Vehicle Recovery
1563 
Alarm Only
390 
Active Disabling
 
109

Length

Max length34
Median length14
Mean length20.026994
Min length10

Characters and Unicode

Total characters99414
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 2902
58.5%
Passive Disabling-Vehicle Recovery 1563
31.5%
Alarm Only 390
 
7.9%
Active Disabling 109
 
2.2%

Length

2023-01-27T11:18:39.218747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:39.323333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
not 2902
25.3%
applicable 2902
25.3%
passive 1563
13.6%
disabling-vehicle 1563
13.6%
recovery 1563
13.6%
alarm 390
 
3.4%
only 390
 
3.4%
active 109
 
0.9%
disabling 109
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 10826
 
10.9%
l 9819
 
9.9%
i 9481
 
9.5%
6527
 
6.6%
a 6527
 
6.6%
c 6137
 
6.2%
p 5804
 
5.8%
s 4798
 
4.8%
b 4574
 
4.6%
o 4465
 
4.5%
Other values (16) 30456
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 78270
78.7%
Uppercase Letter 13054
 
13.1%
Space Separator 6527
 
6.6%
Dash Punctuation 1563
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10826
13.8%
l 9819
12.5%
i 9481
12.1%
a 6527
8.3%
c 6137
7.8%
p 5804
7.4%
s 4798
 
6.1%
b 4574
 
5.8%
o 4465
 
5.7%
v 3235
 
4.1%
Other values (7) 12604
16.1%
Uppercase Letter
ValueCountFrequency (%)
A 3401
26.1%
N 2902
22.2%
D 1672
12.8%
P 1563
12.0%
V 1563
12.0%
R 1563
12.0%
O 390
 
3.0%
Space Separator
ValueCountFrequency (%)
6527
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1563
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 91324
91.9%
Common 8090
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10826
11.9%
l 9819
 
10.8%
i 9481
 
10.4%
a 6527
 
7.1%
c 6137
 
6.7%
p 5804
 
6.4%
s 4798
 
5.3%
b 4574
 
5.0%
o 4465
 
4.9%
A 3401
 
3.7%
Other values (14) 25492
27.9%
Common
ValueCountFrequency (%)
6527
80.7%
- 1563
 
19.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99414
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10826
 
10.9%
l 9819
 
9.9%
i 9481
 
9.5%
6527
 
6.6%
a 6527
 
6.6%
c 6137
 
6.2%
p 5804
 
5.8%
s 4798
 
4.8%
b 4574
 
4.6%
o 4465
 
4.5%
Other values (16) 30456
30.6%

Vehicle_Passive_Restraint
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Y
3648 
N
1314 
Unknown
 
2

Length

Max length7
Median length1
Mean length1.0024174
Min length1

Characters and Unicode

Total characters4976
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowY
3rd rowN
4th rowY
5th rowN

Common Values

ValueCountFrequency (%)
Y 3648
73.5%
N 1314
 
26.5%
Unknown 2
 
< 0.1%

Length

2023-01-27T11:18:39.429372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:39.530904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
y 3648
73.5%
n 1314
 
26.5%
unknown 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
Y 3648
73.3%
N 1314
 
26.4%
n 6
 
0.1%
U 2
 
< 0.1%
k 2
 
< 0.1%
o 2
 
< 0.1%
w 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4964
99.8%
Lowercase Letter 12
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 6
50.0%
k 2
 
16.7%
o 2
 
16.7%
w 2
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
Y 3648
73.5%
N 1314
 
26.5%
U 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 4976
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 3648
73.3%
N 1314
 
26.4%
n 6
 
0.1%
U 2
 
< 0.1%
k 2
 
< 0.1%
o 2
 
< 0.1%
w 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 3648
73.3%
N 1314
 
26.4%
n 6
 
0.1%
U 2
 
< 0.1%
k 2
 
< 0.1%
o 2
 
< 0.1%
w 2
 
< 0.1%

Vehicle_Age_In_Years
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0672844
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:39.605337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5778832
Coefficient of variation (CV)0.36476291
Kurtosis-0.3812779
Mean7.0672844
Median Absolute Deviation (MAD)0
Skewness-1.023117
Sum35082
Variance6.645482
MonotonicityNot monotonic
2023-01-27T11:18:39.690542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
9 2682
54.0%
7 360
 
7.3%
8 305
 
6.1%
6 300
 
6.0%
5 294
 
5.9%
4 284
 
5.7%
3 282
 
5.7%
2 275
 
5.5%
1 182
 
3.7%
ValueCountFrequency (%)
1 182
 
3.7%
2 275
 
5.5%
3 282
 
5.7%
4 284
 
5.7%
5 294
 
5.9%
6 300
 
6.0%
7 360
 
7.3%
8 305
 
6.1%
9 2682
54.0%
ValueCountFrequency (%)
9 2682
54.0%
8 305
 
6.1%
7 360
 
7.3%
6 300
 
6.0%
5 294
 
5.9%
4 284
 
5.7%
3 282
 
5.7%
2 275
 
5.5%
1 182
 
3.7%

Vehicle_Med_Pay_Limit
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2945.5635
Minimum-1
Maximum50000
Zeros0
Zeros (%)0.0%
Negative1223
Negative (%)24.6%
Memory size38.9 KiB
2023-01-27T11:18:39.784131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11000
median1000
Q35000
95-th percentile10000
Maximum50000
Range50001
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation6826.4463
Coefficient of variation (CV)2.317535
Kurtosis34.897784
Mean2945.5635
Median Absolute Deviation (MAD)1001
Skewness5.6521495
Sum14621777
Variance46600369
MonotonicityNot monotonic
2023-01-27T11:18:39.869696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1000 2209
44.5%
-1 1223
24.6%
5000 1126
22.7%
2000 147
 
3.0%
10000 134
 
2.7%
50000 81
 
1.6%
25000 44
 
0.9%
ValueCountFrequency (%)
-1 1223
24.6%
1000 2209
44.5%
2000 147
 
3.0%
5000 1126
22.7%
10000 134
 
2.7%
25000 44
 
0.9%
50000 81
 
1.6%
ValueCountFrequency (%)
50000 81
 
1.6%
25000 44
 
0.9%
10000 134
 
2.7%
5000 1126
22.7%
2000 147
 
3.0%
1000 2209
44.5%
-1 1223
24.6%

Vehicle_Bodily_Injury_Limit
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing194
Missing (%)3.9%
Memory size38.9 KiB
25-50
1706 
100-300
1590 
50-100
1210 
250-500
239 
300-500
 
11
Other values (2)
 
14

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters33390
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25-50
2nd row25-50
3rd row250-500
4th row100-300
5th row25-50

Common Values

ValueCountFrequency (%)
25-50 1706
34.4%
100-300 1590
32.0%
50-100 1210
24.4%
250-500 239
 
4.8%
300-500 11
 
0.2%
500-500 10
 
0.2%
100-500 4
 
0.1%
(Missing) 194
 
3.9%

Length

2023-01-27T11:18:39.960725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:40.070341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
25-50 1706
35.8%
100-300 1590
33.3%
50-100 1210
25.4%
250-500 239
 
5.0%
300-500 11
 
0.2%
500-500 10
 
0.2%
100-500 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 12513
37.5%
5 5135
15.4%
- 4770
 
14.3%
4622
 
13.8%
1 2804
 
8.4%
2 1945
 
5.8%
3 1601
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23998
71.9%
Dash Punctuation 4770
 
14.3%
Space Separator 4622
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12513
52.1%
5 5135
21.4%
1 2804
 
11.7%
2 1945
 
8.1%
3 1601
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 4770
100.0%
Space Separator
ValueCountFrequency (%)
4622
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12513
37.5%
5 5135
15.4%
- 4770
 
14.3%
4622
 
13.8%
1 2804
 
8.4%
2 1945
 
5.8%
3 1601
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12513
37.5%
5 5135
15.4%
- 4770
 
14.3%
4622
 
13.8%
1 2804
 
8.4%
2 1945
 
5.8%
3 1601
 
4.8%

Vehicle_Physical_Damage_Limit
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48251.371
Minimum-1
Maximum500000
Zeros0
Zeros (%)0.0%
Negative194
Negative (%)3.9%
Memory size38.9 KiB
2023-01-27T11:18:40.158966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile25000
Q125000
median50000
Q350000
95-th percentile100000
Maximum500000
Range500001
Interquartile range (IQR)25000

Descriptive statistics

Standard deviation37679.762
Coefficient of variation (CV)0.78090552
Kurtosis45.986396
Mean48251.371
Median Absolute Deviation (MAD)25000
Skewness4.9227169
Sum2.3951981 × 108
Variance1.4197644 × 109
MonotonicityNot monotonic
2023-01-27T11:18:40.242576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
50000 1967
39.6%
25000 1718
34.6%
100000 723
 
14.6%
35000 312
 
6.3%
-1 194
 
3.9%
250000 40
 
0.8%
500000 10
 
0.2%
ValueCountFrequency (%)
-1 194
 
3.9%
25000 1718
34.6%
35000 312
 
6.3%
50000 1967
39.6%
100000 723
 
14.6%
250000 40
 
0.8%
500000 10
 
0.2%
ValueCountFrequency (%)
500000 10
 
0.2%
250000 40
 
0.8%
100000 723
 
14.6%
50000 1967
39.6%
35000 312
 
6.3%
25000 1718
34.6%
-1 194
 
3.9%

Vehicle_Comprehensive_Coverage_Indicator
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4776 
True
 
188
ValueCountFrequency (%)
False 4776
96.2%
True 188
 
3.8%
2023-01-27T11:18:40.895916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Vehicle_Comprehensive_Coverage_Limit
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6158.3852
Minimum-1
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative4776
Negative (%)96.2%
Memory size38.9 KiB
2023-01-27T11:18:40.967556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile-1
Maximum1000000
Range1000001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42766.892
Coefficient of variation (CV)6.9444977
Kurtosis138.83619
Mean6158.3852
Median Absolute Deviation (MAD)0
Skewness10.519941
Sum30570224
Variance1.829007 × 109
MonotonicityNot monotonic
2023-01-27T11:18:41.059194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1 4776
96.2%
75000 81
 
1.6%
100000 61
 
1.2%
300000 24
 
0.5%
500000 20
 
0.4%
1000000 1
 
< 0.1%
200000 1
 
< 0.1%
ValueCountFrequency (%)
-1 4776
96.2%
75000 81
 
1.6%
100000 61
 
1.2%
200000 1
 
< 0.1%
300000 24
 
0.5%
500000 20
 
0.4%
1000000 1
 
< 0.1%
ValueCountFrequency (%)
1000000 1
 
< 0.1%
500000 20
 
0.4%
300000 24
 
0.5%
200000 1
 
< 0.1%
100000 61
 
1.2%
75000 81
 
1.6%
-1 4776
96.2%

Vehicle_Collision_Coverage_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
3005 
False
1959 
ValueCountFrequency (%)
True 3005
60.5%
False 1959
39.5%
2023-01-27T11:18:41.158232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Vehicle_Collision_Coverage_Deductible
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294.4583
Minimum-1
Maximum3000
Zeros0
Zeros (%)0.0%
Negative1959
Negative (%)39.5%
Memory size38.9 KiB
2023-01-27T11:18:41.228888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median250
Q3500
95-th percentile500
Maximum3000
Range3001
Interquartile range (IQR)501

Descriptive statistics

Standard deviation276.66934
Coefficient of variation (CV)0.9395875
Kurtosis1.5361792
Mean294.4583
Median Absolute Deviation (MAD)250
Skewness0.62228351
Sum1461691
Variance76545.922
MonotonicityNot monotonic
2023-01-27T11:18:41.306865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
500 2235
45.0%
-1 1959
39.5%
250 528
 
10.6%
1000 205
 
4.1%
100 27
 
0.5%
200 7
 
0.1%
50 1
 
< 0.1%
3000 1
 
< 0.1%
2000 1
 
< 0.1%
ValueCountFrequency (%)
-1 1959
39.5%
50 1
 
< 0.1%
100 27
 
0.5%
200 7
 
0.1%
250 528
 
10.6%
500 2235
45.0%
1000 205
 
4.1%
2000 1
 
< 0.1%
3000 1
 
< 0.1%
ValueCountFrequency (%)
3000 1
 
< 0.1%
2000 1
 
< 0.1%
1000 205
 
4.1%
500 2235
45.0%
250 528
 
10.6%
200 7
 
0.1%
100 27
 
0.5%
50 1
 
< 0.1%
-1 1959
39.5%

Driver_Total
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
4368 
2
595 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 4368
88.0%
2 595
 
12.0%
3 1
 
< 0.1%

Length

2023-01-27T11:18:41.404523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:41.499781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4368
88.0%
2 595
 
12.0%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4368
88.0%
2 595
 
12.0%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4368
88.0%
2 595
 
12.0%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4368
88.0%
2 595
 
12.0%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4368
88.0%
2 595
 
12.0%
3 1
 
< 0.1%

Driver_Total_Male
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
2626 
0
2311 
2
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2626
52.9%
0 2311
46.6%
2 27
 
0.5%

Length

2023-01-27T11:18:41.578397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:41.674053image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2626
52.9%
0 2311
46.6%
2 27
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 2626
52.9%
0 2311
46.6%
2 27
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2626
52.9%
0 2311
46.6%
2 27
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2626
52.9%
0 2311
46.6%
2 27
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2626
52.9%
0 2311
46.6%
2 27
 
0.5%

Driver_Total_Female
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
2799 
0
2124 
2
 
41

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2799
56.4%
0 2124
42.8%
2 41
 
0.8%

Length

2023-01-27T11:18:41.754701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:41.852361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2799
56.4%
0 2124
42.8%
2 41
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 2799
56.4%
0 2124
42.8%
2 41
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2799
56.4%
0 2124
42.8%
2 41
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2799
56.4%
0 2124
42.8%
2 41
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2799
56.4%
0 2124
42.8%
2 41
 
0.8%

Driver_Total_Single
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3796 
1
1148 
2
 
20

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3796
76.5%
1 1148
 
23.1%
2 20
 
0.4%

Length

2023-01-27T11:18:41.932236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:42.026856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3796
76.5%
1 1148
 
23.1%
2 20
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 3796
76.5%
1 1148
 
23.1%
2 20
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3796
76.5%
1 1148
 
23.1%
2 20
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3796
76.5%
1 1148
 
23.1%
2 20
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3796
76.5%
1 1148
 
23.1%
2 20
 
0.4%

Driver_Total_Married
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
2554 
0
1940 
2
470 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 2554
51.5%
0 1940
39.1%
2 470
 
9.5%

Length

2023-01-27T11:18:42.107446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:42.202087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2554
51.5%
0 1940
39.1%
2 470
 
9.5%

Most occurring characters

ValueCountFrequency (%)
1 2554
51.5%
0 1940
39.1%
2 470
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2554
51.5%
0 1940
39.1%
2 470
 
9.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2554
51.5%
0 1940
39.1%
2 470
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2554
51.5%
0 1940
39.1%
2 470
 
9.5%

Driver_Total_Related_To_Insured_Self
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
3801 
0
1158 
2
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3801
76.6%
0 1158
 
23.3%
2 5
 
0.1%

Length

2023-01-27T11:18:42.287676image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:42.386322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3801
76.6%
0 1158
 
23.3%
2 5
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 3801
76.6%
0 1158
 
23.3%
2 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3801
76.6%
0 1158
 
23.3%
2 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3801
76.6%
0 1158
 
23.3%
2 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3801
76.6%
0 1158
 
23.3%
2 5
 
0.1%

Driver_Total_Related_To_Insured_Spouse
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3693 
1
1270 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3693
74.4%
1 1270
 
25.6%
2 1
 
< 0.1%

Length

2023-01-27T11:18:42.475918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:42.586459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3693
74.4%
1 1270
 
25.6%
2 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 3693
74.4%
1 1270
 
25.6%
2 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3693
74.4%
1 1270
 
25.6%
2 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3693
74.4%
1 1270
 
25.6%
2 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3693
74.4%
1 1270
 
25.6%
2 1
 
< 0.1%

Driver_Total_Related_To_Insured_Child
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4602 
1
 
359
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4602
92.7%
1 359
 
7.2%
2 3
 
0.1%

Length

2023-01-27T11:18:42.674087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:42.775144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4602
92.7%
1 359
 
7.2%
2 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4602
92.7%
1 359
 
7.2%
2 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4602
92.7%
1 359
 
7.2%
2 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4602
92.7%
1 359
 
7.2%
2 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4602
92.7%
1 359
 
7.2%
2 3
 
0.1%

Driver_Total_Licensed_In_State
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
1
4359 
2
592 
0
 
12
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 4359
87.8%
2 592
 
11.9%
0 12
 
0.2%
3 1
 
< 0.1%

Length

2023-01-27T11:18:42.854117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:42.950477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4359
87.8%
2 592
 
11.9%
0 12
 
0.2%
3 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 4359
87.8%
2 592
 
11.9%
0 12
 
0.2%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4359
87.8%
2 592
 
11.9%
0 12
 
0.2%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4359
87.8%
2 592
 
11.9%
0 12
 
0.2%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4359
87.8%
2 592
 
11.9%
0 12
 
0.2%
3 1
 
< 0.1%

Driver_Minimum_Age
Real number (ℝ)

Distinct77
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.088437
Minimum16
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:43.056077image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile20
Q135
median46
Q357
95-th percentile73
Maximum93
Range77
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.818078
Coefficient of variation (CV)0.34321143
Kurtosis-0.47380257
Mean46.088437
Median Absolute Deviation (MAD)11
Skewness0.22185883
Sum228783
Variance250.2116
MonotonicityNot monotonic
2023-01-27T11:18:43.182144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 141
 
2.8%
39 132
 
2.7%
51 128
 
2.6%
48 119
 
2.4%
49 119
 
2.4%
45 118
 
2.4%
47 115
 
2.3%
35 114
 
2.3%
46 114
 
2.3%
44 112
 
2.3%
Other values (67) 3752
75.6%
ValueCountFrequency (%)
16 38
0.8%
17 45
0.9%
18 54
1.1%
19 69
1.4%
20 50
1.0%
21 56
1.1%
22 45
0.9%
23 50
1.0%
24 54
1.1%
25 69
1.4%
ValueCountFrequency (%)
93 1
 
< 0.1%
91 3
 
0.1%
90 2
 
< 0.1%
89 4
 
0.1%
88 4
 
0.1%
87 8
0.2%
86 10
0.2%
85 12
0.2%
84 11
0.2%
83 17
0.3%

Driver_Maximum_Age
Real number (ℝ)

Distinct77
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.02921
Minimum16
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:43.320811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile21
Q136
median47
Q358
95-th percentile74
Maximum93
Range77
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.593548
Coefficient of variation (CV)0.33157154
Kurtosis-0.45073315
Mean47.02921
Median Absolute Deviation (MAD)11
Skewness0.20578789
Sum233453
Variance243.15873
MonotonicityNot monotonic
2023-01-27T11:18:43.459706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 144
 
2.9%
39 139
 
2.8%
51 132
 
2.7%
49 132
 
2.7%
48 126
 
2.5%
45 123
 
2.5%
52 123
 
2.5%
43 119
 
2.4%
42 115
 
2.3%
35 114
 
2.3%
Other values (67) 3697
74.5%
ValueCountFrequency (%)
16 27
0.5%
17 32
0.6%
18 40
0.8%
19 59
1.2%
20 42
0.8%
21 50
1.0%
22 42
0.8%
23 45
0.9%
24 51
1.0%
25 59
1.2%
ValueCountFrequency (%)
93 1
 
< 0.1%
91 4
 
0.1%
90 2
 
< 0.1%
89 4
 
0.1%
88 4
 
0.1%
87 8
0.2%
86 10
0.2%
85 12
0.2%
84 11
0.2%
83 17
0.3%

Driver_Total_Teenager_Age_15_19
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4758 
1
 
204
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4758
95.9%
1 204
 
4.1%
2 2
 
< 0.1%

Length

2023-01-27T11:18:43.582301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:43.677871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4758
95.9%
1 204
 
4.1%
2 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4758
95.9%
1 204
 
4.1%
2 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4758
95.9%
1 204
 
4.1%
2 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4758
95.9%
1 204
 
4.1%
2 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4758
95.9%
1 204
 
4.1%
2 2
 
< 0.1%

Driver_Total_College_Ages_20_23
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4761 
1
 
199
2
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4761
95.9%
1 199
 
4.0%
2 4
 
0.1%

Length

2023-01-27T11:18:43.761950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:43.861606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4761
95.9%
1 199
 
4.0%
2 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4761
95.9%
1 199
 
4.0%
2 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4761
95.9%
1 199
 
4.0%
2 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4761
95.9%
1 199
 
4.0%
2 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4761
95.9%
1 199
 
4.0%
2 4
 
0.1%

Driver_Total_Young_Adult_Ages_24_29
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4539 
1
 
390
2
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4539
91.4%
1 390
 
7.9%
2 35
 
0.7%

Length

2023-01-27T11:18:43.943168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:44.038813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4539
91.4%
1 390
 
7.9%
2 35
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4539
91.4%
1 390
 
7.9%
2 35
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4539
91.4%
1 390
 
7.9%
2 35
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4539
91.4%
1 390
 
7.9%
2 35
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4539
91.4%
1 390
 
7.9%
2 35
 
0.7%

Driver_Total_Low_Middle_Adult_Ages_30_39
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3959 
1
944 
2
 
61

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 3959
79.8%
1 944
 
19.0%
2 61
 
1.2%

Length

2023-01-27T11:18:44.114799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:44.217638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3959
79.8%
1 944
 
19.0%
2 61
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 3959
79.8%
1 944
 
19.0%
2 61
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3959
79.8%
1 944
 
19.0%
2 61
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3959
79.8%
1 944
 
19.0%
2 61
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3959
79.8%
1 944
 
19.0%
2 61
 
1.2%

Driver_Total_Middle_Adult_Ages_40_49
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3720 
1
1175 
2
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3720
74.9%
1 1175
 
23.7%
2 69
 
1.4%

Length

2023-01-27T11:18:44.298215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:44.392927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3720
74.9%
1 1175
 
23.7%
2 69
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 3720
74.9%
1 1175
 
23.7%
2 69
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3720
74.9%
1 1175
 
23.7%
2 69
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3720
74.9%
1 1175
 
23.7%
2 69
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3720
74.9%
1 1175
 
23.7%
2 69
 
1.4%

Driver_Total_Adult_Ages_50_64
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
3514 
1
1339 
2
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3514
70.8%
1 1339
 
27.0%
2 111
 
2.2%

Length

2023-01-27T11:18:44.472495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:44.566501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3514
70.8%
1 1339
 
27.0%
2 111
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 3514
70.8%
1 1339
 
27.0%
2 111
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3514
70.8%
1 1339
 
27.0%
2 111
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3514
70.8%
1 1339
 
27.0%
2 111
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3514
70.8%
1 1339
 
27.0%
2 111
 
2.2%

Driver_Total_Senior_Ages_65_69
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4666 
1
 
285
2
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4666
94.0%
1 285
 
5.7%
2 13
 
0.3%

Length

2023-01-27T11:18:44.648083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:44.742670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4666
94.0%
1 285
 
5.7%
2 13
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4666
94.0%
1 285
 
5.7%
2 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4666
94.0%
1 285
 
5.7%
2 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4666
94.0%
1 285
 
5.7%
2 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4666
94.0%
1 285
 
5.7%
2 13
 
0.3%

Driver_Total_Upper_Senior_Ages_70_plus
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4550 
1
 
393
2
 
21

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4550
91.7%
1 393
 
7.9%
2 21
 
0.4%

Length

2023-01-27T11:18:44.822608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:44.922217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4550
91.7%
1 393
 
7.9%
2 21
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 4550
91.7%
1 393
 
7.9%
2 21
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4550
91.7%
1 393
 
7.9%
2 21
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4550
91.7%
1 393
 
7.9%
2 21
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4550
91.7%
1 393
 
7.9%
2 21
 
0.4%

Vehicle_Youthful_Driver_Indicator
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4478 
True
486 
ValueCountFrequency (%)
False 4478
90.2%
True 486
 
9.8%
2023-01-27T11:18:45.000778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Vehicle_Youthful_Driver_Training_Code
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Not Applicable
4478 
With or Without Driver Training
 
233
Without Driver Training
 
214
With Driver Training
 
39

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters153884
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Applicable
2nd rowNot Applicable
3rd rowNot Applicable
4th rowNot Applicable
5th rowNot Applicable

Common Values

ValueCountFrequency (%)
Not Applicable 4478
90.2%
With or Without Driver Training 233
 
4.7%
Without Driver Training 214
 
4.3%
With Driver Training 39
 
0.8%

Length

2023-01-27T11:18:45.078336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:45.186777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
not 4478
41.2%
applicable 4478
41.2%
driver 486
 
4.5%
training 486
 
4.5%
without 447
 
4.1%
with 272
 
2.5%
or 233
 
2.1%

Most occurring characters

ValueCountFrequency (%)
84183
54.7%
p 8956
 
5.8%
l 8956
 
5.8%
i 6655
 
4.3%
t 5644
 
3.7%
o 5158
 
3.4%
a 4964
 
3.2%
e 4964
 
3.2%
N 4478
 
2.9%
b 4478
 
2.9%
Other values (11) 15448
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 84183
54.7%
Lowercase Letter 59054
38.4%
Uppercase Letter 10647
 
6.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 8956
15.2%
l 8956
15.2%
i 6655
11.3%
t 5644
9.6%
o 5158
8.7%
a 4964
8.4%
e 4964
8.4%
b 4478
7.6%
c 4478
7.6%
r 1691
 
2.9%
Other values (5) 3110
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
N 4478
42.1%
A 4478
42.1%
W 719
 
6.8%
D 486
 
4.6%
T 486
 
4.6%
Space Separator
ValueCountFrequency (%)
84183
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 84183
54.7%
Latin 69701
45.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
p 8956
12.8%
l 8956
12.8%
i 6655
9.5%
t 5644
8.1%
o 5158
7.4%
a 4964
7.1%
e 4964
7.1%
N 4478
6.4%
b 4478
6.4%
c 4478
6.4%
Other values (10) 10970
15.7%
Common
ValueCountFrequency (%)
84183
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84183
54.7%
p 8956
 
5.8%
l 8956
 
5.8%
i 6655
 
4.3%
t 5644
 
3.7%
o 5158
 
3.4%
a 4964
 
3.2%
e 4964
 
3.2%
N 4478
 
2.9%
b 4478
 
2.9%
Other values (11) 15448
 
10.0%

Vehicle_Youthful_Good_Student_Code
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Not Eligible for Good Student Credit
4902 
Eligible for Good Student Credit
 
62

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters178704
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Eligible for Good Student Credit
2nd rowNot Eligible for Good Student Credit
3rd rowNot Eligible for Good Student Credit
4th rowNot Eligible for Good Student Credit
5th rowNot Eligible for Good Student Credit

Common Values

ValueCountFrequency (%)
Not Eligible for Good Student Credit 4902
98.8%
Eligible for Good Student Credit 62
 
1.2%

Length

2023-01-27T11:18:45.283315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:45.384848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
eligible 4964
16.7%
for 4964
16.7%
good 4964
16.7%
student 4964
16.7%
credit 4964
16.7%
not 4902
16.5%

Most occurring characters

ValueCountFrequency (%)
25006
14.0%
t 19794
11.1%
o 19794
11.1%
e 14892
 
8.3%
i 14892
 
8.3%
d 14892
 
8.3%
l 9928
 
5.6%
r 9928
 
5.6%
G 4964
 
2.8%
n 4964
 
2.8%
Other values (8) 39650
22.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 128940
72.2%
Space Separator 25006
 
14.0%
Uppercase Letter 24758
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 19794
15.4%
o 19794
15.4%
e 14892
11.5%
i 14892
11.5%
d 14892
11.5%
l 9928
7.7%
r 9928
7.7%
n 4964
 
3.8%
u 4964
 
3.8%
f 4964
 
3.8%
Other values (2) 9928
7.7%
Uppercase Letter
ValueCountFrequency (%)
G 4964
20.1%
S 4964
20.1%
C 4964
20.1%
E 4964
20.1%
N 4902
19.8%
Space Separator
ValueCountFrequency (%)
25006
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 153698
86.0%
Common 25006
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 19794
12.9%
o 19794
12.9%
e 14892
9.7%
i 14892
9.7%
d 14892
9.7%
l 9928
 
6.5%
r 9928
 
6.5%
G 4964
 
3.2%
n 4964
 
3.2%
u 4964
 
3.2%
Other values (7) 34686
22.6%
Common
ValueCountFrequency (%)
25006
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178704
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25006
14.0%
t 19794
11.1%
o 19794
11.1%
e 14892
 
8.3%
i 14892
 
8.3%
d 14892
 
8.3%
l 9928
 
5.6%
r 9928
 
5.6%
G 4964
 
2.8%
n 4964
 
2.8%
Other values (8) 39650
22.2%

Vehicle_Driver_Points
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4442 
1
 
441
2
 
72
3
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4442
89.5%
1 441
 
8.9%
2 72
 
1.5%
3 9
 
0.2%

Length

2023-01-27T11:18:45.468241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:45.573567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4442
89.5%
1 441
 
8.9%
2 72
 
1.5%
3 9
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4442
89.5%
1 441
 
8.9%
2 72
 
1.5%
3 9
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4442
89.5%
1 441
 
8.9%
2 72
 
1.5%
3 9
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4442
89.5%
1 441
 
8.9%
2 72
 
1.5%
3 9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4442
89.5%
1 441
 
8.9%
2 72
 
1.5%
3 9
 
0.2%

Vehicle_Safe_Driver_Discount_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
2927 
False
2037 
ValueCountFrequency (%)
True 2927
59.0%
False 2037
41.0%
2023-01-27T11:18:45.660035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EEA_Liability_Coverage_Only_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3602 
True
1362 
ValueCountFrequency (%)
False 3602
72.6%
True 1362
 
27.4%
2023-01-27T11:18:45.741569image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EEA_Multi_Auto_Policies_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4384 
False
580 
ValueCountFrequency (%)
True 4384
88.3%
False 580
 
11.7%
2023-01-27T11:18:45.821796image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EEA_Policy_Zip_Code_3
Categorical

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
428
628 
430
547 
424
497 
429
403 
433
341 
Other values (22)
2548 

Length

Max length7
Median length3
Mean length3.0217566
Min length3

Characters and Unicode

Total characters15000
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row426
2nd row428
3rd row429
4th row430
5th row424

Common Values

ValueCountFrequency (%)
428 628
12.7%
430 547
11.0%
424 497
10.0%
429 403
 
8.1%
433 341
 
6.9%
425 330
 
6.6%
441 281
 
5.7%
438 279
 
5.6%
439 231
 
4.7%
427 226
 
4.6%
Other values (17) 1201
24.2%

Length

2023-01-27T11:18:45.909322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
428 628
12.7%
430 547
11.0%
424 497
10.0%
429 403
 
8.1%
433 341
 
6.9%
425 330
 
6.6%
441 281
 
5.7%
438 279
 
5.6%
439 231
 
4.7%
427 226
 
4.6%
Other values (17) 1201
24.2%

Most occurring characters

ValueCountFrequency (%)
4 5946
39.6%
2 2620
17.5%
3 2465
16.4%
8 908
 
6.1%
0 687
 
4.6%
9 634
 
4.2%
1 526
 
3.5%
5 489
 
3.3%
6 272
 
1.8%
7 264
 
1.8%
Other values (5) 189
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14811
98.7%
Lowercase Letter 162
 
1.1%
Uppercase Letter 27
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5946
40.1%
2 2620
17.7%
3 2465
16.6%
8 908
 
6.1%
0 687
 
4.6%
9 634
 
4.3%
1 526
 
3.6%
5 489
 
3.3%
6 272
 
1.8%
7 264
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
n 81
50.0%
k 27
 
16.7%
o 27
 
16.7%
w 27
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14811
98.7%
Latin 189
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
4 5946
40.1%
2 2620
17.7%
3 2465
16.6%
8 908
 
6.1%
0 687
 
4.6%
9 634
 
4.3%
1 526
 
3.6%
5 489
 
3.3%
6 272
 
1.8%
7 264
 
1.8%
Latin
ValueCountFrequency (%)
n 81
42.9%
U 27
 
14.3%
k 27
 
14.3%
o 27
 
14.3%
w 27
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 5946
39.6%
2 2620
17.5%
3 2465
16.4%
8 908
 
6.1%
0 687
 
4.6%
9 634
 
4.2%
1 526
 
3.5%
5 489
 
3.3%
6 272
 
1.8%
7 264
 
1.8%
Other values (5) 189
 
1.3%

EEA_Policy_Tenure
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6866035
Minimum0
Maximum46.9
Zeros459
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:46.026897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.2
median3.5
Q38
95-th percentile19.47
Maximum46.9
Range46.9
Interquartile range (IQR)6.8

Descriptive statistics

Standard deviation6.4506658
Coefficient of variation (CV)1.1343618
Kurtosis5.2108951
Mean5.6866035
Median Absolute Deviation (MAD)2.6
Skewness2.0359235
Sum28228.3
Variance41.61109
MonotonicityNot monotonic
2023-01-27T11:18:46.145072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 459
 
9.2%
0.5 369
 
7.4%
1 339
 
6.8%
2 273
 
5.5%
1.5 273
 
5.5%
2.5 222
 
4.5%
3 201
 
4.0%
3.5 179
 
3.6%
4 138
 
2.8%
5 112
 
2.3%
Other values (275) 2399
48.3%
ValueCountFrequency (%)
0 459
9.2%
0.1 2
 
< 0.1%
0.2 2
 
< 0.1%
0.3 13
 
0.3%
0.4 3
 
0.1%
0.5 369
7.4%
0.6 13
 
0.3%
0.7 4
 
0.1%
0.8 15
 
0.3%
0.9 4
 
0.1%
ValueCountFrequency (%)
46.9 1
< 0.1%
46.6 1
< 0.1%
45.2 1
< 0.1%
44.5 1
< 0.1%
44 1
< 0.1%
43.5 1
< 0.1%
41.1 1
< 0.1%
40.8 1
< 0.1%
39.2 1
< 0.1%
37.8 1
< 0.1%

EEA_Agency_Type
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
Hybrid
1386 
Preferred
1283 
Standard
1227 
Non-standard
1068 

Length

Max length12
Median length9
Mean length8.5606366
Min length6

Characters and Unicode

Total characters42495
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHybrid
2nd rowStandard
3rd rowNon-standard
4th rowHybrid
5th rowNon-standard

Common Values

ValueCountFrequency (%)
Hybrid 1386
27.9%
Preferred 1283
25.8%
Standard 1227
24.7%
Non-standard 1068
21.5%

Length

2023-01-27T11:18:46.256560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:46.368946image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
hybrid 1386
27.9%
preferred 1283
25.8%
standard 1227
24.7%
non-standard 1068
21.5%

Most occurring characters

ValueCountFrequency (%)
r 7530
17.7%
d 7259
17.1%
a 4590
10.8%
e 3849
9.1%
n 3363
7.9%
t 2295
 
5.4%
H 1386
 
3.3%
b 1386
 
3.3%
i 1386
 
3.3%
y 1386
 
3.3%
Other values (7) 8065
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 36463
85.8%
Uppercase Letter 4964
 
11.7%
Dash Punctuation 1068
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 7530
20.7%
d 7259
19.9%
a 4590
12.6%
e 3849
10.6%
n 3363
9.2%
t 2295
 
6.3%
b 1386
 
3.8%
i 1386
 
3.8%
y 1386
 
3.8%
f 1283
 
3.5%
Other values (2) 2136
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
H 1386
27.9%
P 1283
25.8%
S 1227
24.7%
N 1068
21.5%
Dash Punctuation
ValueCountFrequency (%)
- 1068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 41427
97.5%
Common 1068
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 7530
18.2%
d 7259
17.5%
a 4590
11.1%
e 3849
9.3%
n 3363
8.1%
t 2295
 
5.5%
H 1386
 
3.3%
b 1386
 
3.3%
i 1386
 
3.3%
y 1386
 
3.3%
Other values (6) 6997
16.9%
Common
ValueCountFrequency (%)
- 1068
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 7530
17.7%
d 7259
17.1%
a 4590
10.8%
e 3849
9.1%
n 3363
7.9%
t 2295
 
5.4%
H 1386
 
3.3%
b 1386
 
3.3%
i 1386
 
3.3%
y 1386
 
3.3%
Other values (7) 8065
19.0%

EEA_Packaged_Policy_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
2641 
True
2323 
ValueCountFrequency (%)
False 2641
53.2%
True 2323
46.8%
2023-01-27T11:18:46.471450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EEA_Full_Coverage_Indicator
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
3025 
False
1939 
ValueCountFrequency (%)
True 3025
60.9%
False 1939
39.1%
2023-01-27T11:18:46.557983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

EEA_Prior_Bodily_Injury_Limit
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing194
Missing (%)3.9%
Memory size38.9 KiB
20-50
1706 
100-200
1590 
40-100
1210 
100-400
239 
200-400
 
11
Other values (2)
 
14

Length

Max length7
Median length7
Mean length6.2838574
Min length5

Characters and Unicode

Total characters29974
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20-50
2nd row20-50
3rd row100-400
4th row100-200
5th row20-50

Common Values

ValueCountFrequency (%)
20-50 1706
34.4%
100-200 1590
32.0%
40-100 1210
24.4%
100-400 239
 
4.8%
200-400 11
 
0.2%
300-300 10
 
0.2%
75-300 4
 
0.1%
(Missing) 194
 
3.9%

Length

2023-01-27T11:18:46.660537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:46.790706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
20-50 1706
35.8%
100-200 1590
33.3%
40-100 1210
25.4%
100-400 239
 
5.0%
200-400 11
 
0.2%
300-300 10
 
0.2%
75-300 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 14450
48.2%
- 4770
 
15.9%
2 3307
 
11.0%
1 3039
 
10.1%
5 1710
 
5.7%
4 1460
 
4.9%
1210
 
4.0%
3 24
 
0.1%
7 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23994
80.0%
Dash Punctuation 4770
 
15.9%
Space Separator 1210
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14450
60.2%
2 3307
 
13.8%
1 3039
 
12.7%
5 1710
 
7.1%
4 1460
 
6.1%
3 24
 
0.1%
7 4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4770
100.0%
Space Separator
ValueCountFrequency (%)
1210
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29974
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14450
48.2%
- 4770
 
15.9%
2 3307
 
11.0%
1 3039
 
10.1%
5 1710
 
5.7%
4 1460
 
4.9%
1210
 
4.0%
3 24
 
0.1%
7 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29974
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14450
48.2%
- 4770
 
15.9%
2 3307
 
11.0%
1 3039
 
10.1%
5 1710
 
5.7%
4 1460
 
4.9%
1210
 
4.0%
3 24
 
0.1%
7 4
 
< 0.1%

EEA_PolicyYear
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
2006
4964 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters19856
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2006
2nd row2006
3rd row2006
4th row2006
5th row2006

Common Values

ValueCountFrequency (%)
2006 4964
100.0%

Length

2023-01-27T11:18:46.891282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:46.978492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2006 4964
100.0%

Most occurring characters

ValueCountFrequency (%)
0 9928
50.0%
2 4964
25.0%
6 4964
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19856
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9928
50.0%
2 4964
25.0%
6 4964
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19856
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9928
50.0%
2 4964
25.0%
6 4964
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19856
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9928
50.0%
2 4964
25.0%
6 4964
25.0%

SYS_Renewed
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
True
4411 
False
553 
ValueCountFrequency (%)
True 4411
88.9%
False 553
 
11.1%
2023-01-27T11:18:47.054081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

SYS_New_Business
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4499 
True
465 
ValueCountFrequency (%)
False 4499
90.6%
True 465
 
9.4%
2023-01-27T11:18:47.135542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Annual_Premium
Real number (ℝ)

Distinct1166
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.51988
Minimum0.6
Maximum2336.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:47.227535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile107.12
Q1160.06
median328.6
Q3444.14
95-th percentile665.68
Maximum2336.24
Range2335.64
Interquartile range (IQR)284.08

Descriptive statistics

Standard deviation211.49307
Coefficient of variation (CV)0.62847126
Kurtosis9.7316757
Mean336.51988
Median Absolute Deviation (MAD)147.34
Skewness1.9919128
Sum1670484.7
Variance44729.319
MonotonicityNot monotonic
2023-01-27T11:18:47.347658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.84 39
 
0.8%
124.02 34
 
0.7%
127.2 34
 
0.7%
125.08 33
 
0.7%
133.56 31
 
0.6%
130.38 31
 
0.6%
129.32 29
 
0.6%
138.86 27
 
0.5%
137.8 27
 
0.5%
139.92 26
 
0.5%
Other values (1156) 4653
93.7%
ValueCountFrequency (%)
0.6 1
< 0.1%
0.64 1
< 0.1%
0.88 1
< 0.1%
1.24 1
< 0.1%
2.11 1
< 0.1%
3.71 1
< 0.1%
7.71 1
< 0.1%
10.56 1
< 0.1%
11.43 1
< 0.1%
11.6 1
< 0.1%
ValueCountFrequency (%)
2336.24 1
< 0.1%
2227.06 1
< 0.1%
2179.36 1
< 0.1%
2034.14 1
< 0.1%
2018.24 1
< 0.1%
1880.44 1
< 0.1%
1780.8 1
< 0.1%
1679.04 1
< 0.1%
1671.62 1
< 0.1%
1630.96 1
< 0.1%

Claim_Count
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.9 KiB
0
4715 
1
 
235
2
 
14

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4964
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4715
95.0%
1 235
 
4.7%
2 14
 
0.3%

Length

2023-01-27T11:18:47.457004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T11:18:47.548973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4715
95.0%
1 235
 
4.7%
2 14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4715
95.0%
1 235
 
4.7%
2 14
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4964
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4715
95.0%
1 235
 
4.7%
2 14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 4964
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4715
95.0%
1 235
 
4.7%
2 14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4964
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4715
95.0%
1 235
 
4.7%
2 14
 
0.3%

Loss_Amount
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct230
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.86154
Minimum0
Maximum297025
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:47.642716image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.1625
Maximum297025
Range297025
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4760.6109
Coefficient of variation (CV)16.950028
Kurtosis3069.3756
Mean280.86154
Median Absolute Deviation (MAD)0
Skewness51.005066
Sum1394196.7
Variance22663417
MonotonicityNot monotonic
2023-01-27T11:18:47.762309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4715
95.0%
54.5 15
 
0.3%
49.05 4
 
0.1%
38.15 3
 
0.1%
1090 2
 
< 0.1%
3264.82 1
 
< 0.1%
584.99 1
 
< 0.1%
2441.98 1
 
< 0.1%
3073.8 1
 
< 0.1%
1624.91 1
 
< 0.1%
Other values (220) 220
 
4.4%
ValueCountFrequency (%)
0 4715
95.0%
27.25 1
 
< 0.1%
38.15 3
 
0.1%
49.05 4
 
0.1%
54.5 15
 
0.3%
76.98 1
 
< 0.1%
103.55 1
 
< 0.1%
106.28 1
 
< 0.1%
196.76 1
 
< 0.1%
217.67 1
 
< 0.1%
ValueCountFrequency (%)
297025 1
< 0.1%
68571.73 1
< 0.1%
68110.03 1
< 0.1%
57491.51 1
< 0.1%
54500 1
< 0.1%
44814.41 1
< 0.1%
29497.57 1
< 0.1%
27250 1
< 0.1%
23630.11 1
< 0.1%
22054.48 1
< 0.1%

Frequency
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct31
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15825855
Minimum0
Maximum121.95122
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:47.877693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.85
Maximum121.95122
Range121.95122
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.1236301
Coefficient of variation (CV)13.418738
Kurtosis2455.8945
Mean0.15825855
Median Absolute Deviation (MAD)0
Skewness46.516476
Sum785.59546
Variance4.5098047
MonotonicityNot monotonic
2023-01-27T11:18:47.987223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4715
95.0%
2 198
 
4.0%
4 12
 
0.2%
1 11
 
0.2%
4.135649297 2
 
< 0.1%
4.160599126 1
 
< 0.1%
1.289823294 1
 
< 0.1%
4.112687641 1
 
< 0.1%
6.963788301 1
 
< 0.1%
14.14427157 1
 
< 0.1%
Other values (21) 21
 
0.4%
ValueCountFrequency (%)
0 4715
95.0%
1 11
 
0.2%
1.076658053 1
 
< 0.1%
1.185114956 1
 
< 0.1%
1.289823294 1
 
< 0.1%
1.721763085 1
 
< 0.1%
2 198
 
4.0%
2.257846015 1
 
< 0.1%
2.596053998 1
 
< 0.1%
2.710761724 1
 
< 0.1%
ValueCountFrequency (%)
121.9512195 1
< 0.1%
72.46376812 1
< 0.1%
26.31578947 1
< 0.1%
14.14427157 1
< 0.1%
10.06036217 1
< 0.1%
8.298755187 1
< 0.1%
6.963788301 1
< 0.1%
6.944444444 1
< 0.1%
5.485463522 1
< 0.1%
4.160599126 1
< 0.1%

Severity
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct230
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.49566
Minimum0
Maximum297025
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:48.110845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.1625
Maximum297025
Range297025
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4737.8234
Coefficient of variation (CV)17.386785
Kurtosis3128.9412
Mean272.49566
Median Absolute Deviation (MAD)0
Skewness51.693284
Sum1352668.4
Variance22446970
MonotonicityNot monotonic
2023-01-27T11:18:48.228455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4715
95.0%
54.5 15
 
0.3%
49.05 4
 
0.1%
38.15 3
 
0.1%
1090 2
 
< 0.1%
3264.82 1
 
< 0.1%
584.99 1
 
< 0.1%
2441.98 1
 
< 0.1%
3073.8 1
 
< 0.1%
1624.91 1
 
< 0.1%
Other values (220) 220
 
4.4%
ValueCountFrequency (%)
0 4715
95.0%
27.25 1
 
< 0.1%
38.15 3
 
0.1%
49.05 4
 
0.1%
51.775 1
 
< 0.1%
53.14 1
 
< 0.1%
54.5 15
 
0.3%
76.98 1
 
< 0.1%
159.46 1
 
< 0.1%
196.76 1
 
< 0.1%
ValueCountFrequency (%)
297025 1
< 0.1%
68571.73 1
< 0.1%
68110.03 1
< 0.1%
57491.51 1
< 0.1%
54500 1
< 0.1%
44814.41 1
< 0.1%
27250 1
< 0.1%
23630.11 1
< 0.1%
22054.48 1
< 0.1%
15245.03 1
< 0.1%

Loss_Ratio
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct231
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1518151
Minimum0
Maximum1121.88
Zeros4715
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size38.9 KiB
2023-01-27T11:18:48.360407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0595
Maximum1121.88
Range1121.88
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.36933
Coefficient of variation (CV)21.157328
Kurtosis1806.6884
Mean1.1518151
Median Absolute Deviation (MAD)0
Skewness40.759031
Sum5717.61
Variance593.86422
MonotonicityNot monotonic
2023-01-27T11:18:48.481016image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4715
95.0%
0.16 5
 
0.1%
0.13 3
 
0.1%
0.83 3
 
0.1%
5.57 2
 
< 0.1%
0.39 2
 
< 0.1%
3.08 2
 
< 0.1%
5.39 2
 
< 0.1%
3.18 2
 
< 0.1%
0.08 2
 
< 0.1%
Other values (221) 226
 
4.6%
ValueCountFrequency (%)
0 4715
95.0%
0.07 2
 
< 0.1%
0.08 2
 
< 0.1%
0.09 1
 
< 0.1%
0.12 1
 
< 0.1%
0.13 3
 
0.1%
0.14 1
 
< 0.1%
0.15 1
 
< 0.1%
0.16 5
 
0.1%
0.19 1
 
< 0.1%
ValueCountFrequency (%)
1121.88 1
< 0.1%
1112.87 1
< 0.1%
475.69 1
< 0.1%
262.35 1
< 0.1%
212.76 1
< 0.1%
148.87 1
< 0.1%
136.26 1
< 0.1%
115.52 1
< 0.1%
113.5 1
< 0.1%
78.9 1
< 0.1%

Interactions

2023-01-27T11:18:32.235181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:44.233873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:46.810977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:49.127434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.494122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-27T11:18:07.501426image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.611812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:11.728410image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:13.876033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.409119image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:18.573689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:20.745984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:22.791691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:24.879037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:27.586585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:30.081074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.332231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-27T11:17:49.269906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.612644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:00.796876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:02.906821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:05.410846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:07.604033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.714424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:11.831177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:13.984096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.510661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:18.676269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:20.839540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:22.889078image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:24.987617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:27.704205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:30.183628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.438848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:44.524310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:47.036240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:49.419758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.735198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:00.897397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:03.023612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:05.525032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:07.710591image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.824174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:11.934734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:14.091658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.618195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:18.788822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:20.943087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:22.989190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:25.099243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:27.891227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:30.289185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.552394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:44.672639image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:47.169758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:49.583086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.850706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:01.020548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:03.151176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:05.648596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:07.829115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.939724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:12.059444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:14.214222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-27T11:18:28.064388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:30.409774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.655267image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:44.803023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:47.290423image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:49.748981image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.949096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:01.118109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:03.265725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:05.757210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:07.935349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:10.047129image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:12.162120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:14.319829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.852325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:19.018888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:21.165491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:23.206322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:25.324854image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:28.216583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:30.515030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.762838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:44.930884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:47.401289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:49.924046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:59.060627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:01.219670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:03.384326image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:05.869842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:08.045927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:10.154675image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:12.274542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:14.432434image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.963904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:19.133760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:21.267125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-27T11:18:07.153620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.280338image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:11.388870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:13.539664image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.061831image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:18.230752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:20.396767image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:22.467948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:24.513226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:27.164511image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:29.749323image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:31.899228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:34.094236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:46.553596image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:48.884265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.221811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:00.470738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:02.571718image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:04.781000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:07.271153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.393898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:11.501773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:13.650249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.176385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:18.346502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:20.518927image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:22.578545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:24.620759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:27.276064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:29.859939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.013835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:34.204048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:46.688802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:49.011944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:17:58.367354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:00.591821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:02.682351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:04.900788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:07.388779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:09.503193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:11.616276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:13.764836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:16.293974image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:18.462132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:20.633438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:22.687156image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:24.739519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:27.395533image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:29.974523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T11:18:32.126653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-27T11:18:48.697223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
PolicyNoVehicle_TerritoryVehicle_Make_YearVehicle_New_Cost_AmountVehicle_SymbolVehicle_Miles_To_WorkVehicle_Age_In_YearsVehicle_Med_Pay_LimitVehicle_Physical_Damage_LimitVehicle_Comprehensive_Coverage_LimitVehicle_Collision_Coverage_DeductibleDriver_Minimum_AgeDriver_Maximum_AgeEEA_Policy_TenureAnnual_PremiumLoss_AmountFrequencySeverityLoss_RatioPolicy_CompanyPolicy_Installment_TermPolicy_Billing_CodePolicy_Method_Of_PaymentPolicy_Reinstatement_Fee_IndicatorVehicle_PerformanceVehicle_Number_Of_Drivers_AssignedVehicle_UsageVehicle_Days_Per_Week_DrivenVehicle_Annual_MilesVehicle_Anti_Theft_DeviceVehicle_Passive_RestraintVehicle_Bodily_Injury_LimitVehicle_Comprehensive_Coverage_IndicatorVehicle_Collision_Coverage_IndicatorDriver_TotalDriver_Total_MaleDriver_Total_FemaleDriver_Total_SingleDriver_Total_MarriedDriver_Total_Related_To_Insured_SelfDriver_Total_Related_To_Insured_SpouseDriver_Total_Related_To_Insured_ChildDriver_Total_Licensed_In_StateDriver_Total_Teenager_Age_15_19Driver_Total_College_Ages_20_23Driver_Total_Young_Adult_Ages_24_29Driver_Total_Low_Middle_Adult_Ages_30_39Driver_Total_Middle_Adult_Ages_40_49Driver_Total_Adult_Ages_50_64Driver_Total_Senior_Ages_65_69Driver_Total_Upper_Senior_Ages_70_plusVehicle_Youthful_Driver_IndicatorVehicle_Youthful_Driver_Training_CodeVehicle_Youthful_Good_Student_CodeVehicle_Driver_PointsVehicle_Safe_Driver_Discount_IndicatorEEA_Liability_Coverage_Only_IndicatorEEA_Multi_Auto_Policies_IndicatorEEA_Policy_Zip_Code_3EEA_Agency_TypeEEA_Packaged_Policy_IndicatorEEA_Full_Coverage_IndicatorEEA_Prior_Bodily_Injury_LimitSYS_RenewedSYS_New_BusinessClaim_Count
PolicyNo1.000-0.0030.0370.0220.082-0.001-0.034-0.0770.0700.008-0.014-0.260-0.266-0.9820.0080.0190.0180.0190.0190.0830.0960.1120.1300.1280.0200.0560.0260.0000.0870.0790.0940.0550.0290.1080.0000.0380.0280.0770.0770.0550.0630.0380.0210.0700.0570.0660.0600.0470.0610.0740.1430.1100.0740.0470.0420.6750.0680.0480.0560.0000.0920.1070.0550.1210.5300.003
Vehicle_Territory-0.0031.0000.0180.0040.019-0.023-0.0250.0150.0660.0440.0010.0030.0030.000-0.006-0.022-0.022-0.022-0.0220.0000.0000.0000.0300.0120.0000.0000.0700.0000.0000.0640.0270.0440.0340.0420.0000.0220.0110.0650.0590.0000.0250.0090.0000.0000.0140.0000.0170.0000.0000.0090.0210.0030.0000.0000.0210.0040.0240.0490.5500.1940.0440.0450.0440.0100.0190.037
Vehicle_Make_Year0.0370.0181.0000.0020.5270.061-0.9150.0990.1630.0490.665-0.114-0.121-0.0350.6940.1000.0990.1000.0980.0840.0030.0170.1180.0390.0700.0060.0540.0000.0160.3440.5340.0830.0470.6430.0000.1210.1320.0380.0650.0700.0710.0200.0000.0000.0390.0450.0520.0000.0280.0330.0670.0420.0260.0000.0350.0220.5260.0320.0830.0260.3710.6380.0830.1050.0370.057
Vehicle_New_Cost_Amount0.0220.0040.0021.000-0.006-0.006-0.005-0.0200.011-0.017-0.0190.0060.002-0.020-0.017-0.022-0.022-0.022-0.0220.0730.0700.0900.0000.0400.0690.0000.0000.0000.0000.0390.0010.0100.0000.0560.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0310.0000.0000.0460.0490.0000.0040.0270.0570.0100.0430.0000.000
Vehicle_Symbol0.0820.0190.527-0.0061.0000.036-0.4630.0470.1370.0300.416-0.156-0.165-0.0840.5040.0810.0810.0810.0800.0700.0340.0250.0800.0000.2360.0520.0300.0000.0000.2260.2960.0640.0000.3920.0000.0600.0560.0110.0110.0650.0380.0060.0420.0000.0220.0410.0650.0000.0350.0210.0930.0380.0230.0000.0120.0540.3410.0160.0430.0220.2550.3870.0640.0590.0120.072
Vehicle_Miles_To_Work-0.001-0.0230.061-0.0060.0361.000-0.037-0.029-0.027-0.0470.066-0.227-0.234-0.0020.1530.0400.0390.0400.0400.0640.0000.0000.1440.0540.0000.0730.5330.0000.0000.0250.0450.0140.0360.0510.0000.0300.0420.0500.0130.0610.0000.0280.0290.0410.0260.0670.0770.0740.0460.0470.1000.0130.0470.0470.0210.0610.0390.0890.0740.0180.0120.0510.0140.0000.0000.026
Vehicle_Age_In_Years-0.034-0.025-0.915-0.005-0.463-0.0371.000-0.083-0.150-0.046-0.5990.0860.0940.038-0.644-0.076-0.075-0.076-0.0750.0510.0310.0480.0750.0160.0760.0320.0360.0420.0000.3580.3590.0770.0590.6060.0000.0760.0810.0430.0590.0470.0640.0280.0000.0100.0000.0490.0300.0000.0260.0170.0430.0430.0350.0000.0280.0380.4730.0270.0160.0280.3540.6020.0770.0900.0870.059
Vehicle_Med_Pay_Limit-0.0770.0150.099-0.0200.047-0.029-0.0831.0000.1000.0480.0860.0420.0460.0800.1670.0120.0120.0120.0120.0580.0000.0000.0460.0320.0260.0000.0500.0000.0520.0440.0560.1110.1100.0820.0000.0130.0110.0350.0000.0000.0000.0300.0000.0350.0000.0000.0200.0000.0220.0000.0180.0210.0090.0000.0030.0000.0860.0000.0640.0200.0690.0910.1110.0320.0370.000
Vehicle_Physical_Damage_Limit0.0700.0660.1630.0110.137-0.027-0.1500.1001.000-0.3490.132-0.038-0.039-0.0690.1910.0130.0130.0140.0130.0760.0410.0270.0920.0170.0370.0470.0600.0000.0000.0880.1080.7010.2180.1180.0040.0110.0000.0150.0400.0320.0070.0340.0000.0380.0000.0000.0160.0000.0000.0000.0450.0350.0210.0410.0000.0950.1460.0370.1000.0460.1590.1090.7010.0000.0000.000
Vehicle_Comprehensive_Coverage_Limit0.0080.0440.049-0.0170.030-0.047-0.0460.048-0.3491.0000.0570.0190.017-0.0060.065-0.007-0.007-0.007-0.0070.0000.0230.0000.0450.0010.0320.0000.0330.0000.0000.0200.0001.0000.7480.0300.0000.0000.0000.0000.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0310.0360.0000.0000.0000.0000.0000.0000.0300.0000.0000.0210.0590.0641.0000.0000.0230.000
Vehicle_Collision_Coverage_Deductible-0.0140.0010.665-0.0190.4160.066-0.5990.0860.1320.0571.000-0.105-0.1120.0150.7010.1040.1030.1040.1030.0620.0400.0600.1590.0420.0790.0430.0510.0000.0000.2490.3060.0940.0530.7940.0000.0720.0770.0280.0490.0100.0380.0500.0000.0400.0720.0680.0670.0000.0290.0320.0880.0890.0780.0230.0250.0380.6040.0170.0290.0000.3820.7870.0940.0870.0470.080
Driver_Minimum_Age-0.2600.003-0.1140.006-0.156-0.2270.0860.042-0.0380.019-0.1051.0000.9650.270-0.215-0.054-0.053-0.054-0.0530.1120.0590.1040.2960.1150.0000.3110.2080.0000.1180.0720.0960.0550.0400.1400.0740.1020.1380.3520.1830.2050.1060.4740.0600.4910.4870.6030.6500.5850.5710.5390.6880.8500.5230.3670.0610.2380.1340.2010.0790.0000.1050.1380.0550.0690.1020.031
Driver_Maximum_Age-0.2660.003-0.1210.002-0.165-0.2340.0940.046-0.0390.017-0.1120.9651.0000.277-0.209-0.060-0.060-0.060-0.0600.1120.0570.1050.2910.1130.0000.2690.2080.0000.1160.0680.0980.0560.0450.1250.0590.0800.0600.3230.2030.2200.1030.4060.0490.4140.4710.5830.6390.6070.5850.5510.7150.7730.4740.3210.0560.2370.1170.1980.0790.0000.0950.1220.0560.0800.1090.038
EEA_Policy_Tenure-0.9820.000-0.035-0.020-0.084-0.0020.0380.080-0.069-0.0060.0150.2700.2771.000-0.006-0.018-0.017-0.018-0.0180.0710.0770.0770.1450.1170.0230.0510.0530.0000.1140.0540.0890.0540.0000.0700.0000.0280.0510.0710.0750.0000.0650.0000.0000.0530.0440.0570.0720.0270.0550.0760.1670.0990.0650.0540.0320.5310.0570.0580.1320.0000.0780.0710.0540.1030.2550.011
Annual_Premium0.008-0.0060.694-0.0170.5040.153-0.6440.1670.1910.0650.701-0.215-0.209-0.0061.0000.1290.1270.1290.1270.1730.5210.4980.1860.0190.0950.0420.1000.0000.0000.2550.3340.0820.0590.8100.0000.0850.0880.1110.0520.0710.0390.1030.0000.1110.1230.0730.0070.0080.0600.0410.0430.2390.1600.0600.1770.1620.6490.0740.0240.0000.4120.8060.0820.2090.1370.088
Loss_Amount0.019-0.0220.100-0.0220.0810.040-0.0760.0120.013-0.0070.104-0.054-0.060-0.0180.1291.0000.9991.0001.0000.0170.0000.0000.0000.0000.0190.0000.0130.0000.0000.0000.0000.0000.0670.0000.0000.0740.0000.0000.0160.0000.0190.0000.0000.0000.0000.0000.0000.0420.0410.0000.0250.0000.0000.0000.0230.0170.0000.0000.0000.0000.0000.0000.0000.0370.0160.108
Frequency0.018-0.0220.099-0.0220.0810.039-0.0750.0120.013-0.0070.103-0.053-0.060-0.0170.1270.9991.0000.9991.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0200.0100.0000.0000.0000.0000.0000.0000.0000.0000.0140.0000.0000.0000.0000.0030.0000.0000.0750.0560.086
Severity0.019-0.0220.100-0.0220.0810.040-0.0760.0120.014-0.0070.104-0.054-0.060-0.0180.1291.0000.9991.0001.0000.0170.0000.0000.0000.0000.0190.0000.0130.0000.0000.0000.0000.0000.0670.0000.0000.0740.0000.0000.0160.0000.0190.0000.0000.0000.0000.0000.0000.0420.0410.0000.0250.0000.0000.0000.0230.0170.0000.0000.0000.0000.0000.0000.0000.0370.0160.108
Loss_Ratio0.019-0.0220.098-0.0220.0800.040-0.0750.0120.013-0.0070.103-0.053-0.060-0.0180.1271.0001.0001.0001.0000.0510.0000.0000.0000.0380.0000.0000.0000.0000.0000.0000.0000.0000.0400.0080.0000.0570.0000.0000.0130.0000.0130.0000.0000.0000.0000.0000.0160.0250.0140.0000.0000.0000.0000.0000.0320.0260.0000.0000.0000.0000.0090.0070.0000.0760.0200.132
Policy_Company0.0830.0000.0840.0730.0700.0640.0510.0580.0760.0000.0620.1120.1120.0710.1730.0170.0000.0170.0511.0000.0240.0320.0750.0210.0000.0000.0180.0000.0090.0310.0550.1020.0000.0890.0000.0000.0000.0450.0670.0000.0000.0000.0000.0000.0360.0350.0000.0230.0600.0240.0920.0210.0260.0190.1480.0970.0890.0910.0590.0000.1240.0900.1020.0780.0290.018
Policy_Installment_Term0.0960.0000.0030.0700.0340.0000.0310.0000.0410.0230.0400.0590.0570.0770.5210.0000.0000.0000.0000.0241.0000.7330.1990.0000.0370.0770.0430.0000.0000.0420.0060.0150.0020.0260.0000.0000.0000.0300.0000.0000.0000.0140.0000.0350.0240.0000.0000.0140.0150.0000.0170.0470.0450.0000.0110.0890.0150.0150.0000.0180.0000.0270.0150.0740.0770.033
Policy_Billing_Code0.1120.0000.0170.0900.0250.0000.0480.0000.0270.0000.0600.1040.1050.0770.4980.0000.0000.0000.0000.0320.7331.0000.1470.0000.0400.1030.0160.0000.0000.0620.0150.0230.0000.0460.0000.0000.0000.0500.0070.0000.0000.0310.0000.0370.0540.0490.0000.0000.0420.0000.0330.0690.0770.0000.0360.1180.0360.0080.0000.0210.0000.0440.0230.0910.0920.037
Policy_Method_Of_Payment0.1300.0300.1180.0000.0800.1440.0750.0460.0920.0450.1590.2960.2910.1450.1860.0000.0000.0000.0000.0750.1990.1471.0000.1870.0280.1210.2610.0000.0000.0910.1370.1540.0440.1610.0000.1090.1010.1010.0850.0340.0000.0230.0000.0000.0380.1140.1420.0770.1160.1010.1870.0330.0400.0190.0730.1000.1360.0320.0600.0000.0160.1600.1540.0240.0410.056
Policy_Reinstatement_Fee_Indicator0.1280.0120.0390.0400.0000.0540.0160.0320.0170.0010.0420.1150.1130.1170.0190.0000.0480.0000.0380.0210.0000.0000.1871.0000.0000.0480.0950.0000.0000.0000.0000.0410.0310.0000.0000.0390.0360.0340.0130.0000.0150.0000.0000.0180.0000.0400.0670.0020.0490.0330.0640.0000.0000.0060.0120.0380.0000.0000.0490.0000.0210.0000.0410.0520.1150.000
Vehicle_Performance0.0200.0000.0700.0690.2360.0000.0760.0260.0370.0320.0790.0000.0000.0230.0950.0190.0000.0190.0000.0000.0370.0400.0280.0001.0000.0000.0070.0000.0000.0760.0780.0560.0230.1160.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0110.0090.0190.0140.0080.0000.0000.0000.0000.0230.0840.0230.0250.0210.0760.1150.0560.0000.0060.035
Vehicle_Number_Of_Drivers_Assigned0.0560.0000.0060.0000.0520.0730.0320.0000.0470.0000.0430.3110.2690.0510.0420.0000.0000.0000.0000.0000.0770.1030.1210.0480.0001.0000.0980.0000.0000.0030.0000.0350.0260.0540.1940.3070.2640.1130.1400.1140.0780.2040.1580.2180.1080.2230.2040.2190.2650.1060.1310.3670.2320.1990.0200.0890.0540.0560.0430.0120.0240.0480.0350.0480.0550.000
Vehicle_Usage0.0260.0700.0540.0000.0300.5330.0360.0500.0600.0330.0510.2080.2080.0530.1000.0130.0000.0130.0000.0180.0430.0160.2610.0950.0070.0981.0000.0000.0000.0380.0630.0980.0780.0340.0000.0760.0720.0820.0740.0690.0090.0610.0000.0650.0330.0970.1100.0660.0610.0860.1740.0630.0670.0750.0240.0750.0470.1240.0870.0230.0800.0320.0980.0350.0000.024
Vehicle_Days_Per_Week_Driven0.0000.0000.0000.0000.0000.0000.0420.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.0000.0000.0000.0130.0590.0000.0000.0000.0000.0000.0000.0110.0000.0000.0000.0000.0000.000
Vehicle_Annual_Miles0.0870.0000.0160.0000.0000.0000.0000.0520.0000.0000.0000.1180.1160.1140.0000.0000.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.0000.0000.0350.0000.0000.0000.0000.0000.0000.000
Vehicle_Anti_Theft_Device0.0790.0640.3440.0390.2260.0250.3580.0440.0880.0200.2490.0720.0680.0540.2550.0000.0000.0000.0000.0310.0420.0620.0910.0000.0760.0030.0380.0000.0001.0000.3410.1030.0490.4590.0010.0940.0960.0220.0370.0500.0600.0200.0120.0250.0000.0320.0350.0120.0080.0290.0570.0290.0180.0000.0310.0670.3570.0470.0940.0280.2740.4540.1030.0700.0750.052
Vehicle_Passive_Restraint0.0940.0270.5340.0010.2960.0450.3590.0560.1080.0000.3060.0960.0980.0890.3340.0000.0000.0000.0000.0550.0060.0150.1370.0000.0780.0000.0630.0000.0000.3411.0000.1210.0000.4920.0000.1030.1080.0000.0000.0550.0570.0000.0000.0000.0110.0290.0430.0000.0100.0310.0780.0000.0120.0000.0470.0600.4190.0400.0790.0150.2780.4870.1210.0600.0000.056
Vehicle_Bodily_Injury_Limit0.0550.0440.0830.0100.0640.0140.0770.1110.7011.0000.0940.0550.0560.0540.0820.0000.0000.0000.0000.1020.0150.0230.1540.0410.0560.0350.0980.0000.0000.1030.1211.0001.0000.1440.0000.0330.0120.0450.0820.0450.0220.0030.0000.0290.0000.0260.0460.0220.0430.0440.0510.0220.0290.0450.0000.1460.1660.0590.0940.0340.2090.1441.0000.0330.0510.000
Vehicle_Comprehensive_Coverage_Indicator0.0290.0340.0470.0000.0000.0360.0590.1100.2180.7480.0530.0400.0450.0000.0590.0670.0000.0670.0400.0000.0020.0000.0440.0310.0230.0260.0780.0000.0000.0490.0001.0001.0000.0450.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0310.0360.0230.0000.0030.0000.0000.0420.0000.0430.0000.0780.0470.0560.0871.0000.0050.0330.000
Vehicle_Collision_Coverage_Indicator0.1080.0420.6430.0560.3920.0510.6060.0820.1180.0300.7940.1400.1250.0700.8100.0000.0000.0000.0080.0890.0260.0460.1610.0000.1160.0540.0340.0000.0000.4590.4920.1440.0451.0000.0000.1560.1650.0620.0780.0260.0910.0960.0000.1070.0410.0560.0630.0090.0350.0000.0330.1220.1330.0420.0750.0330.7610.0600.0770.0000.4620.9910.1440.1030.0790.110
Driver_Total0.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0740.0590.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1940.0000.0000.0000.0010.0000.0000.0000.0001.0000.2440.2770.1290.6190.1190.3380.0720.9990.0620.0510.1650.2130.2280.2900.1060.1270.0360.0490.0310.0000.0000.0000.0390.0600.0190.0190.0000.0000.0110.0250.013
Driver_Total_Male0.0380.0220.1210.0000.0600.0300.0760.0130.0110.0000.0720.1020.0800.0280.0850.0740.0000.0740.0570.0000.0000.0000.1090.0390.0000.3070.0760.0000.0000.0940.1030.0330.0000.1560.2441.0000.5710.1210.2180.2250.0630.1300.2430.1360.0220.0690.0770.0810.1080.0440.0400.1410.1060.0320.0180.0080.1580.1290.1370.0000.0270.1560.0330.0000.0000.037
Driver_Total_Female0.0280.0110.1320.0000.0560.0420.0810.0110.0000.0000.0770.1380.0600.0510.0880.0000.0000.0000.0000.0000.0000.0000.1010.0360.0000.2640.0720.0000.0000.0960.1080.0120.0000.1650.2770.5711.0000.1600.2040.1630.2850.1470.2760.1360.0430.0670.0710.0750.1060.0290.0380.1530.1400.0930.0410.0000.1740.1000.0300.0050.0570.1680.0120.0250.0350.026
Driver_Total_Single0.0770.0650.0380.0000.0110.0500.0430.0350.0150.0000.0280.3520.3230.0710.1110.0000.0000.0000.0000.0450.0300.0500.1010.0340.0000.1130.0820.0000.0000.0220.0000.0450.0000.0620.1290.1210.1601.0000.4420.0360.2090.4190.1300.3360.1880.1160.0430.0410.1240.0650.0880.5310.3790.1990.0410.1400.0470.1320.0460.0090.1720.0610.0450.0140.0510.027
Driver_Total_Married0.0770.0590.0650.0000.0110.0130.0590.0000.0400.0210.0490.1830.2030.0750.0520.0160.0000.0160.0130.0670.0000.0070.0850.0130.0000.1400.0740.0000.0000.0370.0000.0820.0000.0780.6190.2180.2040.4421.0000.1660.4480.1680.6160.1210.1210.1650.2390.2380.3160.1160.1450.3040.2140.0980.0420.1310.0510.2580.0820.0210.2490.0780.0820.0300.0630.022
Driver_Total_Related_To_Insured_Self0.0550.0000.0700.0000.0650.0610.0470.0000.0320.0000.0100.2050.2200.0000.0710.0000.0000.0000.0000.0000.0000.0000.0340.0000.0000.1140.0690.0000.0000.0500.0550.0450.0000.0260.1190.2250.1630.0360.1661.0000.3940.2950.1180.1710.0970.0370.0560.0530.0860.0320.0670.2550.1940.1340.0130.0320.0160.1410.0000.0210.1150.0280.0450.0000.0210.000
Driver_Total_Related_To_Insured_Spouse0.0630.0250.0710.0000.0380.0000.0640.0000.0070.0000.0380.1060.1030.0650.0390.0190.0000.0190.0130.0000.0000.0000.0000.0150.0000.0780.0090.0000.0000.0600.0570.0220.0000.0910.3380.0630.2850.2090.4480.3941.0000.0850.3370.0580.0540.0840.1310.1360.1630.0610.1790.1520.1060.0310.0210.0650.0870.1440.0330.0240.1610.0920.0220.0170.0430.000
Driver_Total_Related_To_Insured_Child0.0380.0090.0200.0000.0060.0280.0280.0300.0340.0000.0500.4740.4060.0000.1030.0000.0000.0000.0000.0000.0140.0310.0230.0000.0000.2040.0610.0000.0000.0200.0000.0030.0000.0960.0720.1300.1470.4190.1680.2950.0851.0000.0720.7120.2310.0880.0540.0480.0940.0460.0480.6740.5010.3200.0370.0290.0860.0650.0000.0140.0200.0920.0030.0000.0000.000
Driver_Total_Licensed_In_State0.0210.0000.0000.0000.0420.0290.0000.0000.0000.0000.0000.0600.0490.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.0120.0000.0000.0000.0000.9990.2430.2760.1300.6160.1180.3370.0721.0000.0610.0500.1580.2130.2280.2910.1070.1200.0380.0390.0280.0000.0000.0000.0380.0470.0120.0200.0000.0000.0170.0340.004
Driver_Total_Teenager_Age_15_190.0700.0000.0000.0000.0000.0410.0100.0350.0380.0000.0400.4910.4140.0530.1110.0000.0000.0000.0000.0000.0350.0370.0000.0180.0000.2180.0650.0000.0000.0250.0000.0290.0000.1070.0620.1360.1360.3360.1210.1710.0580.7120.0611.0000.0120.0350.0530.0320.0860.0310.0400.6140.6180.4600.0520.1050.0800.0360.0000.0200.0410.1050.0290.0000.0330.000
Driver_Total_College_Ages_20_230.0570.0140.0390.0000.0220.0260.0000.0000.0000.0000.0720.4870.4710.0440.1230.0000.0000.0000.0000.0360.0240.0540.0380.0000.0000.1080.0330.0000.0000.0000.0110.0000.0000.0410.0510.0220.0430.1880.1210.0970.0540.2310.0500.0121.0000.0220.0670.0680.0820.0310.0330.5340.4250.0470.0280.0540.0500.0370.0000.0000.0540.0390.0000.0310.0130.000
Driver_Total_Young_Adult_Ages_24_290.0660.0000.0450.0000.0410.0670.0490.0000.0000.0000.0680.6030.5830.0570.0730.0000.0200.0000.0000.0350.0000.0490.1140.0400.0000.2230.0970.0440.0000.0320.0290.0260.0050.0560.1650.0690.0670.1160.1650.0370.0840.0880.1580.0350.0221.0000.0720.1140.1250.0490.0620.1790.2260.0280.0410.0870.0570.0460.0000.0000.0000.0590.0260.0500.0730.010
Driver_Total_Low_Middle_Adult_Ages_30_390.0600.0170.0520.0000.0650.0770.0300.0200.0160.0000.0670.6500.6390.0720.0070.0000.0100.0000.0160.0000.0000.0000.1420.0670.0110.2040.1100.0000.0000.0350.0430.0460.0000.0630.2130.0770.0710.0430.2390.0560.1310.0540.2130.0530.0670.0721.0000.1690.2190.0860.1040.1410.1000.0460.0100.0710.0640.0000.0150.0140.0000.0610.0460.0000.0000.004
Driver_Total_Middle_Adult_Ages_40_490.0470.0000.0000.0000.0000.0740.0000.0000.0000.0000.0000.5850.6070.0270.0080.0420.0000.0420.0250.0230.0140.0000.0770.0020.0090.2190.0660.0000.0000.0120.0000.0220.0310.0090.2280.0810.0750.0410.2380.0530.1360.0480.2280.0320.0680.1140.1691.0000.2270.0970.1160.1270.0920.0070.0000.0470.0190.0580.0000.0000.0340.0060.0220.0000.0130.000
Driver_Total_Adult_Ages_50_640.0610.0000.0280.0000.0350.0460.0260.0220.0000.0310.0290.5710.5850.0550.0600.0410.0000.0410.0140.0600.0150.0420.1160.0490.0190.2650.0610.0000.0000.0080.0100.0430.0360.0350.2900.1080.1060.1240.3160.0860.1630.0940.2910.0860.0820.1250.2190.2271.0000.0750.1280.1880.1320.0690.0410.1070.0380.0570.0920.0220.0290.0400.0430.0490.0430.004
Driver_Total_Senior_Ages_65_690.0740.0090.0330.0160.0210.0470.0170.0000.0000.0360.0320.5390.5510.0760.0410.0000.0000.0000.0000.0240.0000.0000.1010.0330.0140.1060.0860.0000.0000.0290.0310.0440.0230.0000.1060.0440.0290.0650.1160.0320.0610.0460.1070.0310.0310.0490.0860.0970.0751.0000.0210.0810.0540.0200.0130.0760.0000.0270.0020.0000.0350.0000.0440.0170.0250.012
Driver_Total_Upper_Senior_Ages_70_plus0.1430.0210.0670.0000.0930.1000.0430.0180.0450.0000.0880.6880.7150.1670.0430.0250.0000.0250.0000.0920.0170.0330.1870.0640.0080.1310.1740.0000.0240.0570.0780.0510.0000.0330.1270.0400.0380.0880.1450.0670.1790.0480.1200.0400.0330.0620.1040.1160.1280.0211.0000.0920.0620.0270.0000.1360.0480.1710.0970.0140.0700.0300.0510.0260.0690.000
Vehicle_Youthful_Driver_Indicator0.1100.0030.0420.0000.0380.0130.0430.0210.0350.0000.0890.8500.7730.0990.2390.0000.0000.0000.0000.0210.0470.0690.0330.0000.0000.3670.0630.0130.0000.0290.0000.0220.0030.1220.0360.1410.1530.5310.3040.2550.1520.6740.0380.6140.5340.1790.1410.1270.1880.0810.0921.0001.0000.3380.0750.1160.1030.0120.0640.0160.0950.1170.0220.0390.0420.007
Vehicle_Youthful_Driver_Training_Code0.0740.0000.0260.0000.0230.0470.0350.0090.0210.0000.0780.5230.4740.0650.1600.0000.0000.0000.0000.0260.0450.0770.0400.0000.0000.2320.0670.0590.0000.0180.0120.0290.0000.1330.0490.1060.1400.3790.2140.1940.1060.5010.0390.6180.4250.2260.1000.0920.1320.0540.0621.0001.0000.4680.0500.1200.1080.0640.0410.0110.0980.1280.0290.0500.0510.000
Vehicle_Youthful_Good_Student_Code0.0470.0000.0000.0310.0000.0470.0000.0000.0410.0000.0230.3670.3210.0540.0600.0000.0000.0000.0000.0190.0000.0000.0190.0060.0000.1990.0750.0000.0000.0000.0000.0450.0000.0420.0310.0320.0930.1990.0980.1340.0310.3200.0280.4600.0470.0280.0460.0070.0690.0200.0270.3380.4681.0000.0000.0460.0270.0230.0000.0030.0000.0350.0450.0000.0000.000
Vehicle_Driver_Points0.0420.0210.0350.0000.0120.0210.0280.0030.0000.0000.0250.0610.0560.0320.1770.0230.0000.0230.0320.1480.0110.0360.0730.0120.0000.0200.0240.0000.0000.0310.0470.0000.0420.0750.0000.0180.0410.0410.0420.0130.0210.0370.0000.0520.0280.0410.0100.0000.0410.0130.0000.0750.0500.0001.0000.4100.0540.0280.0000.0000.0000.0720.0000.0370.0460.031
Vehicle_Safe_Driver_Discount_Indicator0.6750.0040.0220.0000.0540.0610.0380.0000.0950.0000.0380.2380.2370.5310.1620.0170.0140.0170.0260.0970.0890.1180.1000.0380.0230.0890.0750.0000.0000.0670.0600.1460.0000.0330.0000.0080.0000.1400.1310.0320.0650.0290.0000.1050.0540.0870.0710.0470.1070.0760.1360.1160.1200.0460.4101.0000.0000.0280.0730.0000.0800.0320.1460.1130.2760.040
EEA_Liability_Coverage_Only_Indicator0.0680.0240.5260.0460.3410.0390.4730.0860.1460.0300.6040.1340.1170.0570.6490.0000.0000.0000.0000.0890.0150.0360.1360.0000.0840.0540.0470.0000.0000.3570.4190.1660.0430.7610.0000.1580.1740.0470.0510.0160.0870.0860.0000.0800.0500.0570.0640.0190.0380.0000.0480.1030.1080.0270.0540.0001.0000.0520.0750.0520.5760.7680.1660.0770.0730.105
EEA_Multi_Auto_Policies_Indicator0.0480.0490.0320.0490.0160.0890.0270.0000.0370.0000.0170.2010.1980.0580.0740.0000.0000.0000.0000.0910.0150.0080.0320.0000.0230.0560.1240.0000.0000.0470.0400.0590.0000.0600.0390.1290.1000.1320.2580.1410.1440.0650.0380.0360.0370.0460.0000.0580.0570.0270.1710.0120.0640.0230.0280.0280.0521.0000.0740.0000.3400.0590.0590.0140.0040.000
EEA_Policy_Zip_Code_30.0560.5500.0830.0000.0430.0740.0160.0640.1000.0000.0290.0790.0790.1320.0240.0000.0000.0000.0000.0590.0000.0000.0600.0490.0250.0430.0870.0000.0350.0940.0790.0940.0780.0770.0600.1370.0300.0460.0820.0000.0330.0000.0470.0000.0000.0000.0150.0000.0920.0020.0970.0640.0410.0000.0000.0730.0750.0741.0000.3520.0600.0780.0940.0000.0000.009
EEA_Agency_Type0.0000.1940.0260.0040.0220.0180.0280.0200.0460.0210.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0210.0000.0000.0210.0120.0230.0110.0000.0280.0150.0340.0470.0000.0190.0000.0050.0090.0210.0210.0240.0140.0120.0200.0000.0000.0140.0000.0220.0000.0140.0160.0110.0030.0000.0000.0520.0000.3521.0000.0390.0000.0340.0170.0190.000
EEA_Packaged_Policy_Indicator0.0920.0440.3710.0270.2550.0120.3540.0690.1590.0590.3820.1050.0950.0780.4120.0000.0030.0000.0090.1240.0000.0000.0160.0210.0760.0240.0800.0000.0000.2740.2780.2090.0560.4620.0190.0270.0570.1720.2490.1150.1610.0200.0200.0410.0540.0000.0000.0340.0290.0350.0700.0950.0980.0000.0000.0800.5760.3400.0600.0391.0000.4670.2090.1020.0760.043
EEA_Full_Coverage_Indicator0.1070.0450.6380.0570.3870.0510.6020.0910.1090.0640.7870.1380.1220.0710.8060.0000.0000.0000.0070.0900.0270.0440.1600.0000.1150.0480.0320.0000.0000.4540.4870.1440.0870.9910.0000.1560.1680.0610.0780.0280.0920.0920.0000.1050.0390.0590.0610.0060.0400.0000.0300.1170.1280.0350.0720.0320.7680.0590.0780.0000.4671.0000.1440.1050.0790.109
EEA_Prior_Bodily_Injury_Limit0.0550.0440.0830.0100.0640.0140.0770.1110.7011.0000.0940.0550.0560.0540.0820.0000.0000.0000.0000.1020.0150.0230.1540.0410.0560.0350.0980.0000.0000.1030.1211.0001.0000.1440.0000.0330.0120.0450.0820.0450.0220.0030.0000.0290.0000.0260.0460.0220.0430.0440.0510.0220.0290.0450.0000.1460.1660.0590.0940.0340.2090.1441.0000.0330.0510.000
SYS_Renewed0.1210.0100.1050.0430.0590.0000.0900.0320.0000.0000.0870.0690.0800.1030.2090.0370.0750.0370.0760.0780.0740.0910.0240.0520.0000.0480.0350.0000.0000.0700.0600.0330.0050.1030.0110.0000.0250.0140.0300.0000.0170.0000.0170.0000.0310.0500.0000.0000.0490.0170.0260.0390.0500.0000.0370.1130.0770.0140.0000.0170.1020.1050.0331.0000.1330.022
SYS_New_Business0.5300.0190.0370.0000.0120.0000.0870.0370.0000.0230.0470.1020.1090.2550.1370.0160.0560.0160.0200.0290.0770.0920.0410.1150.0060.0550.0000.0000.0000.0750.0000.0510.0330.0790.0250.0000.0350.0510.0630.0210.0430.0000.0340.0330.0130.0730.0000.0130.0430.0250.0690.0420.0510.0000.0460.2760.0730.0040.0000.0190.0760.0790.0510.1331.0000.000
Claim_Count0.0030.0370.0570.0000.0720.0260.0590.0000.0000.0000.0800.0310.0380.0110.0880.1080.0860.1080.1320.0180.0330.0370.0560.0000.0350.0000.0240.0000.0000.0520.0560.0000.0000.1100.0130.0370.0260.0270.0220.0000.0000.0000.0040.0000.0000.0100.0040.0000.0040.0120.0000.0070.0000.0000.0310.0400.1050.0000.0090.0000.0430.1090.0000.0220.0001.000

Missing values

2023-01-27T11:18:34.513509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-27T11:18:35.026866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-27T11:18:35.536089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

PolicyNoPolicy_CompanyPolicy_Installment_TermPolicy_Billing_CodePolicy_Method_Of_PaymentPolicy_Reinstatement_Fee_IndicatorPolicy_Zip_Code_Garaging_LocationVehicle_TerritoryVehicle_Make_YearVehicle_Make_DescriptionVehicle_PerformanceVehicle_New_Cost_AmountVehicle_SymbolVehicle_Number_Of_Drivers_AssignedVehicle_UsageVehicle_Miles_To_WorkVehicle_Days_Per_Week_DrivenVehicle_Annual_MilesVehicle_Anti_Theft_DeviceVehicle_Passive_RestraintVehicle_Age_In_YearsVehicle_Med_Pay_LimitVehicle_Bodily_Injury_LimitVehicle_Physical_Damage_LimitVehicle_Comprehensive_Coverage_IndicatorVehicle_Comprehensive_Coverage_LimitVehicle_Collision_Coverage_IndicatorVehicle_Collision_Coverage_DeductibleDriver_TotalDriver_Total_MaleDriver_Total_FemaleDriver_Total_SingleDriver_Total_MarriedDriver_Total_Related_To_Insured_SelfDriver_Total_Related_To_Insured_SpouseDriver_Total_Related_To_Insured_ChildDriver_Total_Licensed_In_StateDriver_Minimum_AgeDriver_Maximum_AgeDriver_Total_Teenager_Age_15_19Driver_Total_College_Ages_20_23Driver_Total_Young_Adult_Ages_24_29Driver_Total_Low_Middle_Adult_Ages_30_39Driver_Total_Middle_Adult_Ages_40_49Driver_Total_Adult_Ages_50_64Driver_Total_Senior_Ages_65_69Driver_Total_Upper_Senior_Ages_70_plusVehicle_Youthful_Driver_IndicatorVehicle_Youthful_Driver_Training_CodeVehicle_Youthful_Good_Student_CodeVehicle_Driver_PointsVehicle_Safe_Driver_Discount_IndicatorEEA_Liability_Coverage_Only_IndicatorEEA_Multi_Auto_Policies_IndicatorEEA_Policy_Zip_Code_3EEA_Policy_TenureEEA_Agency_TypeEEA_Packaged_Policy_IndicatorEEA_Full_Coverage_IndicatorEEA_Prior_Bodily_Injury_LimitEEA_PolicyYearSYS_RenewedSYS_New_BusinessAnnual_PremiumClaim_CountLoss_AmountFrequencySeverityLoss_Ratio
0164562033Standard6Direct Billed to InsuredPre-paidN42602311990DODG CARAVAN SEStandard-131Pleasure-15UnknownNot ApplicableN9100025-5025000N-1N-1101010101616100000100NNot ApplicableNot Eligible for Good Student Credit0YYY42616.1HybridNN20-502006YN111.3000.000.00.000.00
1165119133Standard6Direct Billed to InsuredPre-paidN42857352001BUIK REG LS-LSEStandard-1101Pleasure-15UnknownNot ApplicableY6100025-5025000N-1Y250101010101555500000100NNot ApplicableNot Eligible for Good Student Credit0NNY42816.5StandardYY20-502006YN408.1000.000.00.000.00
2165166239Standard6Direct Billed to InsuredPre-paidN42980301977FORD TRUCKStandard-1599Farm-15UnknownNot ApplicableN91000250-500100000N-1N-1110010001474700001000NNot ApplicableNot Eligible for Good Student Credit0YYY42923.0Non-standardNN100-4002006YN125.0800.000.00.000.00
3165198832Standard6Direct Billed to InsuredInstallmentN43050352002TYTA TUNDRA SR5Standard-11599Pleasure-15UnknownNot ApplicableY51000100-30050000N-1Y250110011001373700010000NNot ApplicableNot Eligible for Good Student Credit0YNY43016.0HybridYY100-2002006YN554.3800.000.00.000.00
4165319534Standard6Direct Billed to InsuredPre-paidN42496351992CHEV CAMARO RSSports-1161Pleasure-15UnknownNot ApplicableN9200025-5025000N-1N-1211021102485300001100NNot ApplicableNot Eligible for Good Student Credit0YYY42416.5Non-standardNN20-502006YN129.3200.000.00.000.00
5165355034Standard6Direct Billed to InsuredInstallmentN42361301992FORD RANGERStandard-1599Work155UnknownNot ApplicableN9100025-5025000N-1Y500110011001393900010000NNot ApplicableNot Eligible for Good Student Credit0YNY42316.5HybridYY20-502006YN279.8400.000.00.000.00
6165386232Standard6Direct Billed to InsuredPre-paidN42357371955CHEV BELAIR 2DRStandard-131Pleasure-15UnknownNot ApplicableN9100050-10050000N-1N-1110011001555500000100NNot ApplicableNot Eligible for Good Student Credit0YYY42316.0HybridNN40-1002006NN142.0400.000.00.000.00
7165708632Preferred6Direct Billed to InsuredPre-paidN42916351972CHEV C-10 P-UStandard-141Work15UnknownNot ApplicableN9100025-5025000N-1Y500110011001505000000100NNot ApplicableNot Eligible for Good Student Credit0YNY42916.0StandardNY20-502006YN248.0400.000.00.000.00
8165951132Standard6Direct Billed to InsuredPre-paidN43155311989CHEV PICKUP1500Standard-1111Work225UnknownNot ApplicableN9500025-5025000N-1N-1211021102535600000200NNot ApplicableNot Eligible for Good Student Credit0YNY43116.0Non-standardYN20-502006YN210.9400.000.00.000.00
9165971032Standard6Direct Billed to InsuredInstallmentN43046352003HOND ACCORD LXStandard-1111Work145UnknownPassive Disabling-Vehicle RecoveryY41000100-300100000N-1Y500101011001363600010000NNot ApplicableNot Eligible for Good Student Credit0YNY43016.0StandardNY100-2002006YN432.4812770.882.02770.886.41
PolicyNoPolicy_CompanyPolicy_Installment_TermPolicy_Billing_CodePolicy_Method_Of_PaymentPolicy_Reinstatement_Fee_IndicatorPolicy_Zip_Code_Garaging_LocationVehicle_TerritoryVehicle_Make_YearVehicle_Make_DescriptionVehicle_PerformanceVehicle_New_Cost_AmountVehicle_SymbolVehicle_Number_Of_Drivers_AssignedVehicle_UsageVehicle_Miles_To_WorkVehicle_Days_Per_Week_DrivenVehicle_Annual_MilesVehicle_Anti_Theft_DeviceVehicle_Passive_RestraintVehicle_Age_In_YearsVehicle_Med_Pay_LimitVehicle_Bodily_Injury_LimitVehicle_Physical_Damage_LimitVehicle_Comprehensive_Coverage_IndicatorVehicle_Comprehensive_Coverage_LimitVehicle_Collision_Coverage_IndicatorVehicle_Collision_Coverage_DeductibleDriver_TotalDriver_Total_MaleDriver_Total_FemaleDriver_Total_SingleDriver_Total_MarriedDriver_Total_Related_To_Insured_SelfDriver_Total_Related_To_Insured_SpouseDriver_Total_Related_To_Insured_ChildDriver_Total_Licensed_In_StateDriver_Minimum_AgeDriver_Maximum_AgeDriver_Total_Teenager_Age_15_19Driver_Total_College_Ages_20_23Driver_Total_Young_Adult_Ages_24_29Driver_Total_Low_Middle_Adult_Ages_30_39Driver_Total_Middle_Adult_Ages_40_49Driver_Total_Adult_Ages_50_64Driver_Total_Senior_Ages_65_69Driver_Total_Upper_Senior_Ages_70_plusVehicle_Youthful_Driver_IndicatorVehicle_Youthful_Driver_Training_CodeVehicle_Youthful_Good_Student_CodeVehicle_Driver_PointsVehicle_Safe_Driver_Discount_IndicatorEEA_Liability_Coverage_Only_IndicatorEEA_Multi_Auto_Policies_IndicatorEEA_Policy_Zip_Code_3EEA_Policy_TenureEEA_Agency_TypeEEA_Packaged_Policy_IndicatorEEA_Full_Coverage_IndicatorEEA_Prior_Bodily_Injury_LimitEEA_PolicyYearSYS_RenewedSYS_New_BusinessAnnual_PremiumClaim_CountLoss_AmountFrequencySeverityLoss_Ratio
4954381020700Standard6Direct Billed to InsuredPre-paidN43169302001VLKS JETTA GLSStandard-1161Work125UnknownPassive Disabling-Vehicle RecoveryY71000250-500250000N-1Y500101001001353500010000NNot ApplicableNot Eligible for Good Student Credit0NNY4310.0StandardYY100-4002006YY493.9600.00.00.00.0
4955381039600Standard6Direct Billed to InsuredPre-paidN43065271982DODG D50Standard-181Pleasure-15UnknownNot ApplicableN910000250-500100000N-1N-1101011001454500001000NNot ApplicableNot Eligible for Good Student Credit0YYY4300.0HybridNN100-4002006YY192.9200.00.00.00.0
4956381040600Preferred6Direct Billed to InsuredInstallmentN42846351999LINC NAVIGATORStandard-1161Pleasure-15UnknownPassive Disabling-Vehicle RecoveryY95000NaN-1Y75000Y500110001001686800000010NNot ApplicableNot Eligible for Good Student Credit3NNY4280.0PreferredYYNaN2006NY512.6100.00.00.00.0
4957381052900Standard6Direct Billed to InsuredPre-paidN42385351997TYTA CAMRYStandard-11199Work105UnknownNot ApplicableY91000050-10035000N-1N-1110011001454500001000NNot ApplicableNot Eligible for Good Student Credit0YYY4230.0PreferredNN40-1002006NY84.8600.00.00.00.0
4958381080800Standard6Direct Billed to InsuredInstallmentN42471321999MERC SABLELS-PRStandard-1102Pleasure-15UnknownNot ApplicableY9200025-5025000N-1Y500211101012205801000100YWithout Driver TrainingNot Eligible for Good Student Credit0NNN4240.0PreferredNY20-502006NY189.9400.00.00.00.0
4959381137600Standard6Direct Billed to InsuredInstallmentN42643362001PONT GR PRIX SEStandard-1111Pleasure-15UnknownPassive Disabling-Vehicle RecoveryY7-125-5025000N-1N-1101010101666600000010NNot ApplicableNot Eligible for Good Student Credit0NYY4260.0Non-standardNN20-502006YY140.9800.00.00.00.0
4960381140200Standard6Direct Billed to InsuredInstallmentN43066272007FORD F-250 SUPEStandard-1171Pleasure-15UnknownPassive Disabling-Vehicle RecoveryY1100025-5025000N-1Y500101101001242400100000YWith or Without Driver TrainingNot Eligible for Good Student Credit0YNY4306.0HybridYY20-502006YY594.6600.00.00.00.0
4961381148600Standard6Direct Billed to InsuredInstallmentN42694361998JEEP CHEROKEEStandard-1111Pleasure-15UnknownNot ApplicableY9-150-10035000N-1Y500101101001464600001000NNot ApplicableNot Eligible for Good Student Credit1NNY4260.0Non-standardYY40-1002006YY197.2200.00.00.00.0
4962381184700Standard6Direct Billed to InsuredPre-paidN42498351999HOND CR-V EXStandard-11199Pleasure-15UnknownNot ApplicableY9-1NaN-1Y300000Y500211021102494900002000NNot ApplicableNot Eligible for Good Student Credit0NNY4240.0HybridYYNaN2006NY358.2400.00.00.00.0
4963381258900Standard6Direct Billed to InsuredPre-paidN42891351999FORD F250 SPDTYStandard-11799Pleasure-15UnknownNot ApplicableY91000NaN-1Y100000Y500110101001353500010000NNot ApplicableNot Eligible for Good Student Credit0YNY4280.0PreferredYYNaN2006YY484.4200.00.00.00.0